Assignment1-Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Name:** LI XINYAN\n",
    "\n",
    "**EID:** 55670594\n",
    "\n",
    "**Kaggle Team Name:** Saaries98"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS5489 - Assignment 1 - Tweet Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal\n",
    "In this assignment, the task is predict the sentiment of Tweets about four technology companies, Apple, Microsoft, Google, and Twitter. Here are some examples of tweets with different sentiments:\n",
    "  - **positive**: _\"http://t.co/QV4m1Un9 Forget the phone.. Nice UI. Liking the Scroll Feature #android #google #nexus\"_\n",
    "  - **negative**: _\"Have never had such poor customer service at @Apple before! What happened? (@ Apple Store w/ 2 others) http://t.co/GKlXMUi6\"_\n",
    "  - **neutral**: _\"The lock screen now has facial recognition capability! #Google #Android #ICS\"_.\n",
    "\n",
    "Your goal is to train a classifier to predict whether a tweet is positive, neutral, or negative sentiment.\n",
    "\n",
    "\n",
    "## Methodology\n",
    "You need to train classifiers using the training data, and then predict on the test data. You are free to choose the feature extraction method and classifier algorithm.  You are free to use methods that were not introduced in class.  You should probably do cross-validation to select a good parameters.\n",
    "\n",
    "\n",
    "## Evaluation on Kaggle\n",
    "\n",
    "You need to submit your test predictions to Kaggle for evaluation.  50% of the test data will be used to show your ranking on the live leaderboard.  After the assignment deadline, the remaining 50% will be used to calculate your final ranking. Also the top-ranked entries will be asked to give a short 5 minute presentation on what they did.\n",
    "\n",
    "To submit to Kaggle you need to create an account, and use the competition invitation that will be posted on Canvas.\n",
    "\n",
    "**Note:** You can only submit 2 times per day to Kaggle!\n",
    "\n",
    "## What to hand in\n",
    "You need to turn in the following things:\n",
    "\n",
    "1. This ipynb file with your source code and documentation. _**You should write about all the various attempts that you make to find a good solution.**_ You may also submit python scripts as source code, but your documentation must be in the ipynb file.\n",
    "2. Your final submission file to Kaggle.\n",
    "3. The ipynb file `Assignment1-Final.ipynb`, which contains the code that generates the final submission file that you submit to Kaggle.  **This code will be used to verify that your Kaggle submission is reproducible.**\n",
    "\n",
    "Files should be uploaded to Assignment 1 on Canvas.\n",
    "\n",
    "## Grading\n",
    "The marks of the assignment are distributed as follows:\n",
    "- 45% - Results using various classifiers and feature representations.\n",
    "- 30% - Trying out feature representations (e.g. adding additional features) or classifiers not used in the tutorials/lectures.\n",
    "- 20% - Quality of the written report.  More points for insightful observations and analysis.\n",
    "- 5% - Final ranking on the Kaggle test data (private leaderboard). If a submission cannot be reproduced by the submitted code, it will not receive marks for ranking.\n",
    "- **Late Penalty:** 25 marks will be subtracted for each day late.\n",
    "\n",
    "**Note:** you should start early! Some classifiers may take a while to train.\n",
    "\n",
    "\n",
    "## Kaggle Notebooks\n",
    "\n",
    "If you like, you can use Kaggle notebooks to run your code. Note that you still need to submit your code to Canvas for grading.\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training data is in the text file `sanders_tweets_train.txt`.  This CSV file contains the  tweet, labels, and topics. The class labels are: `\"positive\"`, `\"neutral\"`, and `\"negative\"`. The training data also includes the corresponding tweet topics: `\"apple\"`, `\"microsoft\"`, `\"google\"`, and `\"twitter\"`.\n",
    "\n",
    "The testing data is in the text file `sanders_tweets_test.txt`, and only contains the tweets.  The topics are not included, since they are not known at test time.\n",
    "\n",
    "To submit to Kaggle, you need to generate a Kaggle submission files, which is CSV file with the following format: \n",
    "\n",
    "<pre>\n",
    "Id,Prediction\n",
    "1,neutral\n",
    "2,neutral\n",
    "3,positive\n",
    "4,negative\n",
    "...\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are two helpful functions for reading the text data and writing the Kaggle submission file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import IPython.core.display         \n",
    "# setup output image format (Chrome works best)\n",
    "IPython.core.display.set_matplotlib_formats(\"svg\")\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from numpy import *\n",
    "from sklearn import *\n",
    "from scipy import stats\n",
    "import csv\n",
    "random.seed(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_text_data(fname):\n",
    "    txtdata = []\n",
    "    classes = []\n",
    "    topics  = []\n",
    "    with open(fname, 'r') as csvfile:\n",
    "        reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n",
    "        for row in reader:\n",
    "            # get the text\n",
    "            txtdata.append(row[0])\n",
    "            # get the class (convert to integer)\n",
    "            if len(row)>1:\n",
    "                classes.append(row[1])\n",
    "                topics.append(row[2])\n",
    "    \n",
    "    if (len(classes)>0) and (len(txtdata) != len(classes)):        \n",
    "        raise Exception(\"mismatched length!\")\n",
    "    \n",
    "    return (txtdata, classes, topics)\n",
    "\n",
    "def write_csv_kaggle_sub(fname, Y):\n",
    "    # fname = file name\n",
    "    # Y is a list/array with class entries\n",
    "    \n",
    "    # header\n",
    "    tmp = [['Id', 'Prediction']]\n",
    "    \n",
    "    # add ID numbers for each Y\n",
    "    for (i,y) in enumerate(Y):\n",
    "        tmp2 = [(i+1), y]\n",
    "        tmp.append(tmp2)\n",
    "        \n",
    "    # write CSV file\n",
    "    with open(fname, 'w') as f:\n",
    "        writer = csv.writer(f)\n",
    "        writer.writerows(tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below code will load the training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2396\n",
      "1028\n"
     ]
    }
   ],
   "source": [
    "# load the data\n",
    "# (if using Kaggle notebooks you need to include the directory path: /kaggle/input/cs5489-2020b-assignment-1/)\n",
    "(traintxt, trainY, traintopic) = read_text_data(\"sanders_tweets_train.txt\")\n",
    "(testtxt, _, _)                = read_text_data(\"sanders_tweets_test.txt\")\n",
    "\n",
    "print(len(traintxt))\n",
    "print(len(testtxt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['negative' 'neutral' 'positive']\n"
     ]
    }
   ],
   "source": [
    "classnames = unique(trainY)\n",
    "print(classnames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example to write a csv file with predictions on the test set.  These are random predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write your predictions on the test set\n",
    "i = random.randint(len(classnames), size=len(testtxt))\n",
    "predY = classnames[i]\n",
    "write_csv_kaggle_sub(\"my_submission.csv\", predY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive\n",
      "google\n",
      "http://t.co/QV4m1Un9 Forget the phone.. Nice UI. Liking the Scroll Feature #android #google #nexus\n"
     ]
    }
   ],
   "source": [
    "print(trainY[0])\n",
    "print(traintopic[0])\n",
    "print(traintxt[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# YOUR CODE and DOCUMENTATION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Exploration\n",
    "\n",
    "1. Except for building the TF-IDF or the BoW feature, I extracted some meta information for my classifier to make more accurate predictions.\n",
    "\n",
    "2. For example: I counted the number of exclamation(!), questionmark(?), hashtag(#), quote(\"), full stop(.), mention(@).\n",
    "\n",
    "3. Some NLP library provides easy-to-use API to evaluate the sentiment of s sentence, so I made use of them as one of the feature: Affin [https://github.com/fnielsen/afinn], TextBlob [https://textblob.readthedocs.io/en/dev/].\n",
    "\n",
    "4. During the data distribution analysis, I found that the tweets corpus has special preference for specific company (there are more positive tweets about Google but more negative tweets about Apple).\n",
    "\n",
    "5. **The following section shows how I explore the database and determine what features to use.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data pre-processing\n",
    "\n",
    "### I use pandas to reduce the processing workload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "c1 = np.array(traintxt)\n",
    "c2 = np.array(traintopic)\n",
    "c3 = np.array(trainY)\n",
    "\n",
    "dix = {\"traintxt\": c1,\n",
    "       \"traintopic\":c2,\n",
    "       \"trainY\":c3}\n",
    "\n",
    "train_data = pd.DataFrame(dix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>traintxt</th>\n",
       "      <th>traintopic</th>\n",
       "      <th>trainY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://t.co/QV4m1Un9 Forget the phone.. Nice U...</td>\n",
       "      <td>google</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I hate when my phne do what it want on #twitter</td>\n",
       "      <td>twitter</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>…and only the first 23 images in my Photo Roll...</td>\n",
       "      <td>apple</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The lock screen now has facial recognition cap...</td>\n",
       "      <td>google</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#TeamGoogleNexus RT @B__Y #Google + #Samsung =...</td>\n",
       "      <td>google</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>@karth_vader_ when did you change your nick ag...</td>\n",
       "      <td>twitter</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>GoodNight #Twitter:)</td>\n",
       "      <td>twitter</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>@AndroidPolice Duly noted -- I hope AOSP is up...</td>\n",
       "      <td>google</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2394</th>\n",
       "      <td>#Microsoft's plan for bringing its #BI tools t...</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>yeah im gon #twitter but fuck wit me....</td>\n",
       "      <td>twitter</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2396 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               traintxt traintopic    trainY\n",
       "0     http://t.co/QV4m1Un9 Forget the phone.. Nice U...     google  positive\n",
       "1       I hate when my phne do what it want on #twitter    twitter   neutral\n",
       "2     …and only the first 23 images in my Photo Roll...      apple  negative\n",
       "3     The lock screen now has facial recognition cap...     google   neutral\n",
       "4     #TeamGoogleNexus RT @B__Y #Google + #Samsung =...     google   neutral\n",
       "...                                                 ...        ...       ...\n",
       "2391  @karth_vader_ when did you change your nick ag...    twitter   neutral\n",
       "2392                               GoodNight #Twitter:)    twitter   neutral\n",
       "2393  @AndroidPolice Duly noted -- I hope AOSP is up...     google   neutral\n",
       "2394  #Microsoft's plan for bringing its #BI tools t...  microsoft   neutral\n",
       "2395           yeah im gon #twitter but fuck wit me....    twitter   neutral\n",
       "\n",
       "[2396 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See what we have\n",
    "display(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/opt/anaconda3/bin/python'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which python Jupyter Notebook is using, then install package for it\n",
    "import sys\n",
    "sys.executable\n",
    "# /opt/anaconda3/bin/python -m pip install afinn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Functions\n",
    "\n",
    "### Helper functions for data pre-processing\n",
    "\n",
    "1. eva_afinn_score(): calculate the Afinn sentiment score\n",
    "2. eva_blob_score(): calculate the TextBlob sentiment score\n",
    "3. add_extra_feature(): the punctuation might relate to sentiment, so we extract them as features\n",
    "4. eva_comp(): the mentioned company name might relate to sentiment, so we extract them as features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from afinn import Afinn\n",
    "from textblob import TextBlob\n",
    "\n",
    "def eva_afinn_score(string):\n",
    "    af = Afinn(emoticons=True)\n",
    "    scr = af.score(string)\n",
    "    return scr\n",
    "\n",
    "\n",
    "def eva_blob_score(string):\n",
    "    scr = TextBlob(string).sentiment.polarity\n",
    "    return scr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def add_extra_feature(df, tweet_column):\n",
    "    \n",
    "    # Number of \"\"\n",
    "    df['number_of_quote'] = tweet_column.apply(lambda x: (len(re.findall(r'\"', x))/2))\n",
    "    \n",
    "    # Number of .\n",
    "    df['number_of_stop'] = tweet_column.apply(lambda x: (len(re.findall(r'.', x))))\n",
    "    \n",
    "    # Number of !\n",
    "    df['number_of_exclamation'] = tweet_column.apply(lambda x: (len(re.findall(r'!', x))))\n",
    "    \n",
    "    # Number of ?\n",
    "    df['number_of_questionmark'] = tweet_column.apply(lambda x: (len(re.findall(r'[?]', x))))\n",
    "    \n",
    "    # Number of #\n",
    "    df['number_of_hashtag'] = tweet_column.apply(lambda x: (len(re.findall(r'#', x))))\n",
    "    \n",
    "    # Number of @\n",
    "    df['number_of_mention'] = tweet_column.apply(lambda x: (len(re.findall(r'@', x))))\n",
    "    \n",
    "    # Number of @\n",
    "    df['number_of_comma'] = tweet_column.apply(lambda x: (len(re.findall(r',', x))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eva_comp(df, tweet_column):\n",
    "    df['apple'] = tweet_column.apply(lambda x: int('apple' in x))\n",
    "    df['google'] = tweet_column.apply(lambda x: int('google' in x))\n",
    "    df['twitter'] = tweet_column.apply(lambda x: int('twitter' in x))\n",
    "    df['microsoft'] = tweet_column.apply(lambda x: int('microsoft' in x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper functions for data cleaning, tokenize, and stemming\n",
    "5. clean_str(): remove the unwanted noise/punctuation/symbol in our data corpus\n",
    "6. tokenize(): tokenize the tweet sentences into words\n",
    "7. stemming(): apply stemming to words\n",
    "8. word_to_sen(): transform word lists back to sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_str(string):\n",
    "    # Special emoji for heart\n",
    "    string = re.sub(\"&lt;3\",\"love\", string)\n",
    "    \n",
    "    # Remove hyperlink\n",
    "    string = re.sub(\"http://[\\w|./]*\",\"\", string)\n",
    "    \n",
    "    # Only keep alphabet\n",
    "    string = re.sub(r\"[^A-Za-z]\", \" \", string)\n",
    "    \n",
    "#     string = re.sub(r'(.)\\1+', r'\\1\\1', string) \n",
    "#     string = re.sub(r\"\\'s\", \" \\'s\", string)\n",
    "#     string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n",
    "#     string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n",
    "#     string = re.sub(r\"\\'re\", \" \\'re\", string)\n",
    "#     string = re.sub(r\"\\'d\", \" \\'d\", string)\n",
    "#     string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n",
    "#     string = re.sub(r\"!\", \" ! \", string)\n",
    "#     string = re.sub(r\"\\)\", \" \\) \", string)\n",
    "#     string = re.sub(r\"\\?\", \" \\? \", string)\n",
    "#     string = re.sub(r\"\\s{2,}\", \" \", string)\n",
    "\n",
    "    # return lower case\n",
    "    return string.strip().lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "def tokenize(text):\n",
    "    return word_tokenize(text)\n",
    "\n",
    "def stemming(words):\n",
    "    ps = PorterStemmer()\n",
    "    stem_words = []\n",
    "    for w in words:\n",
    "        w = ps.stem(w)\n",
    "        stem_words.append(w)\n",
    "    return stem_words\n",
    "\n",
    "def word_to_sen(words):\n",
    "    sen = ''\n",
    "    for w in words:\n",
    "        sen = sen + w + ' '\n",
    "    return sen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply the above help functions to our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function: 1~3\n",
    "add_extra_feature(train_data, train_data[\"traintxt\"])\n",
    "train_data['affin'] = train_data['traintxt'].apply(eva_afinn_score)\n",
    "train_data['blob'] = train_data['traintxt'].apply(eva_blob_score)\n",
    "\n",
    "# Helper function: 5\n",
    "train_data['traintxt'] = train_data['traintxt'].apply(clean_str)\n",
    "\n",
    "# Helper function: 4\n",
    "eva_comp(train_data, train_data[\"traintxt\"])\n",
    "\n",
    "# Helper function: 6~8\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(tokenize)\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(stemming)\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(word_to_sen)\n",
    "\n",
    "# The tweets length might also be helpeful\n",
    "train_data['length'] =  train_data[\"traintxt\"].apply(lambda x: (len(x)))"
   ]
  },
  {
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       "      <td>goodnight twitter</td>\n",
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       "                                               traintxt traintopic    trainY  \\\n",
       "0     forget the phone nice ui like the scroll featu...     google  positive   \n",
       "1       i hate when my phne do what it want on twitter     twitter   neutral   \n",
       "2     and onli the first imag in my photo roll made ...      apple  negative   \n",
       "3     the lock screen now ha facial recognit capabl ...     google   neutral   \n",
       "4     teamgooglenexu rt b y googl samsung perfect ic...     google   neutral   \n",
       "...                                                 ...        ...       ...   \n",
       "2391  karth vader when did you chang your nick again...    twitter   neutral   \n",
       "2392                                 goodnight twitter     twitter   neutral   \n",
       "2393  androidpolic duli note i hope aosp is updat wi...     google   neutral   \n",
       "2394  microsoft s plan for bring it bi tool to io an...  microsoft   neutral   \n",
       "2395               yeah im gon twitter but fuck wit me     twitter   neutral   \n",
       "\n",
       "      number_of_quote  number_of_stop  number_of_exclamation  \\\n",
       "0                 0.0              98                      0   \n",
       "1                 0.0              47                      0   \n",
       "2                 0.0             113                      0   \n",
       "3                 0.0              76                      1   \n",
       "4                 0.0              86                      0   \n",
       "...               ...             ...                    ...   \n",
       "2391              0.0             131                      0   \n",
       "2392              0.0              20                      0   \n",
       "2393              0.0              82                      0   \n",
       "2394              0.0             128                      0   \n",
       "2395              0.0              40                      0   \n",
       "\n",
       "      number_of_questionmark  number_of_hashtag  number_of_mention  \\\n",
       "0                          0                  3                  0   \n",
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       "2                          2                  0                  1   \n",
       "3                          0                  3                  0   \n",
       "4                          0                  5                  1   \n",
       "...                      ...                ...                ...   \n",
       "2391                       2                  1                  1   \n",
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       "2393                       0                  2                  1   \n",
       "2394                       0                  2                  0   \n",
       "2395                       0                  1                  0   \n",
       "\n",
       "      number_of_comma  affin      blob  apple  google  twitter  microsoft  \\\n",
       "0                   0    4.0  0.600000      0       1        0          0   \n",
       "1                   0   -2.0 -0.800000      0       0        1          0   \n",
       "2                   2   -4.0 -0.056667      1       0        0          0   \n",
       "3                   0    3.0  0.000000      0       1        0          0   \n",
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       "...               ...    ...       ...    ...     ...      ...        ...   \n",
       "2391                0   -3.0 -0.500000      0       0        1          0   \n",
       "2392                0    2.0  0.500000      0       0        1          0   \n",
       "2393                0    2.0  0.000000      0       1        0          0   \n",
       "2394                2    0.0  0.500000      0       0        0          1   \n",
       "2395                0   -3.0 -0.400000      0       0        1          0   \n",
       "\n",
       "      length  \n",
       "0         67  \n",
       "1         47  \n",
       "2         99  \n",
       "3         63  \n",
       "4         73  \n",
       "...      ...  \n",
       "2391     120  \n",
       "2392      18  \n",
       "2393      66  \n",
       "2394      82  \n",
       "2395      36  \n",
       "\n",
       "[2396 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ptint pre-processing results\n",
    "display(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Database Visualization and Feature Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The label distribution of the entire training set\n",
    "\n",
    "1. We can see that there are more samples with sentiment == neutral.\n",
    "2. We have similar amount of positive and negative samples.\n",
    "3. I think this distribution might result in an unbalanced classifier, so I try to reduce the impacts of data unbalanced when trying the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
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      "[363, 1630, 403]\n"
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    {
     "data": {
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     },
     "execution_count": 17,
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   "source": [
    "# import seaborn as sns\n",
    "# sns.countplot(x = 'trainY', data = train_data)\n",
    "# plt.title(\"Sentiment distribution\")\n",
    "\n",
    "plt.figure(figsize=(12, 2))\n",
    "labels = [\"positive\", \"neutral\", \"negative\"]\n",
    "\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(train_data[train_data[\"trainY\"] == lab]))\n",
    "\n",
    "print(cnt)\n",
    "    \n",
    "plt.subplot(1,4,1)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Count')\n",
    "plt.title(\"Sentiment distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The distribution of the special punctuations over the labels\n",
    "\n",
    "1. The number of questionmark, quote, and comma are highly connected to positive/negative sentiments.\n",
    "2. The number of hashtag, full stop show less connection to positive/negative sentiments.\n",
    "3. All the tweets must contain at least one of the symbols in [exclamation, questionmark, hashtag, mention].\n",
    "4. Tweets that mention Google, Apple, and Microsoft have special sentiments preference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 864x144 with 4 Axes>"
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "plt.figure(figsize=(12, 2))\n",
    "labels = [\"positive\", \"neutral\", \"negative\"]\n",
    "\n",
    "condition = \"number_of_exclamation\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,4,1)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Exclamation')\n",
    "\n",
    "\n",
    "condition = \"number_of_questionmark\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Questionmark')\n",
    "\n",
    "condition = \"number_of_hashtag\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "x = 0\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "    x += len(filtered_data[filtered_data[\"trainY\"] == lab])\n",
    "    \n",
    "plt.subplot(1,4,3)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Hashtag')\n",
    "\n",
    "\n",
    "condition = \"number_of_mention\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "x = 0\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "    x += len(filtered_data[filtered_data[\"trainY\"] == lab])\n",
    "plt.subplot(1,4,4)\n",
    "\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Mention ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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   "source": [
    "plt.figure(figsize=(9, 2))\n",
    "labels = [\"positive\", \"neutral\", \"negative\"]\n",
    "\n",
    "condition = \"number_of_quote\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Quote')\n",
    "\n",
    "\n",
    "condition = \"number_of_stop\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Full_Stop')\n",
    "\n",
    "condition = \"number_of_comma\"\n",
    "filtered_data = train_data[train_data[condition] > 0]\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Comma')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2396\n",
      "1941\n",
      "1679\n",
      "240\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data))\n",
    "filtered_data = train_data[train_data[\"number_of_exclamation\"] == 0]\n",
    "print(len(filtered_data))\n",
    "\n",
    "filtered_data = filtered_data[filtered_data[\"number_of_questionmark\"] == 0]\n",
    "\n",
    "print(len(filtered_data))\n",
    "filtered_data = filtered_data[filtered_data[\"number_of_hashtag\"] ==0]\n",
    "print(len(filtered_data))\n",
    "\n",
    "filtered_data = filtered_data[filtered_data[\"number_of_mention\"] == 0]\n",
    "print(len(filtered_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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       "<Figure size 864x144 with 4 Axes>"
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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "# Apple, Microsoft, Google, and Twitter\n",
    "\n",
    "plt.figure(figsize=(12, 2))\n",
    "labels = [\"positive\", \"neutral\", \"negative\"]\n",
    "\n",
    "condition = \"apple\"\n",
    "filtered_data = train_data[train_data[\"traintopic\"] == condition]\n",
    "\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,4,1)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Apple')\n",
    "\n",
    "\n",
    "condition = \"microsoft\"\n",
    "filtered_data = train_data[train_data[\"traintopic\"] == condition]\n",
    "cnt = []\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Microsoft')\n",
    "\n",
    "condition = \"google\"\n",
    "filtered_data = train_data[train_data[\"traintopic\"] == condition]\n",
    "cnt = []\n",
    "x = 0\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "    x += len(filtered_data[filtered_data[\"trainY\"] == lab])\n",
    "    \n",
    "plt.subplot(1,4,3)\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Google')\n",
    "\n",
    "\n",
    "condition = \"twitter\"\n",
    "filtered_data = train_data[train_data[\"traintopic\"] == condition]\n",
    "cnt = []\n",
    "x = 0\n",
    "for lab in labels:\n",
    "    cnt.append(len(filtered_data[filtered_data[\"trainY\"] == lab]) / len(train_data[\"trainY\"] == lab))\n",
    "    x += len(filtered_data[filtered_data[\"trainY\"] == lab])\n",
    "plt.subplot(1,4,4)\n",
    "\n",
    "plt.bar(labels, cnt, color=\"#87CEFA\")\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Propotion')\n",
    "plt.title('#Twitter ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Encoding\n",
    "\n",
    "### 1. Use TF-IDF representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
       "      <td>teamgooglenexu rt b y googl samsung perfect ic...</td>\n",
       "      <td>google</td>\n",
       "      <td>neutral</td>\n",
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       "      <td>86</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>karth vader when did you chang your nick again...</td>\n",
       "      <td>twitter</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>goodnight twitter</td>\n",
       "      <td>twitter</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>androidpolic duli note i hope aosp is updat wi...</td>\n",
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       "    <tr>\n",
       "      <th>2394</th>\n",
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       "      <td>microsoft</td>\n",
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       "      <td>128</td>\n",
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       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>yeah im gon twitter but fuck wit me</td>\n",
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       "      <td>40</td>\n",
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       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2396 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               traintxt traintopic    trainY  \\\n",
       "0     forget the phone nice ui like the scroll featu...     google  positive   \n",
       "1       i hate when my phne do what it want on twitter     twitter   neutral   \n",
       "2     and onli the first imag in my photo roll made ...      apple  negative   \n",
       "3     the lock screen now ha facial recognit capabl ...     google   neutral   \n",
       "4     teamgooglenexu rt b y googl samsung perfect ic...     google   neutral   \n",
       "...                                                 ...        ...       ...   \n",
       "2391  karth vader when did you chang your nick again...    twitter   neutral   \n",
       "2392                                 goodnight twitter     twitter   neutral   \n",
       "2393  androidpolic duli note i hope aosp is updat wi...     google   neutral   \n",
       "2394  microsoft s plan for bring it bi tool to io an...  microsoft   neutral   \n",
       "2395               yeah im gon twitter but fuck wit me     twitter   neutral   \n",
       "\n",
       "      number_of_quote  number_of_stop  number_of_exclamation  \\\n",
       "0                 0.0              98                      0   \n",
       "1                 0.0              47                      0   \n",
       "2                 0.0             113                      0   \n",
       "3                 0.0              76                      1   \n",
       "4                 0.0              86                      0   \n",
       "...               ...             ...                    ...   \n",
       "2391              0.0             131                      0   \n",
       "2392              0.0              20                      0   \n",
       "2393              0.0              82                      0   \n",
       "2394              0.0             128                      0   \n",
       "2395              0.0              40                      0   \n",
       "\n",
       "      number_of_questionmark  number_of_hashtag  number_of_mention  \\\n",
       "0                          0                  3                  0   \n",
       "1                          0                  1                  0   \n",
       "2                          2                  0                  1   \n",
       "3                          0                  3                  0   \n",
       "4                          0                  5                  1   \n",
       "...                      ...                ...                ...   \n",
       "2391                       2                  1                  1   \n",
       "2392                       0                  1                  0   \n",
       "2393                       0                  2                  1   \n",
       "2394                       0                  2                  0   \n",
       "2395                       0                  1                  0   \n",
       "\n",
       "      number_of_comma  affin      blob  apple  google  twitter  microsoft  \\\n",
       "0                   0    4.0  0.600000      0       1        0          0   \n",
       "1                   0   -2.0 -0.800000      0       0        1          0   \n",
       "2                   2   -4.0 -0.056667      1       0        0          0   \n",
       "3                   0    3.0  0.000000      0       1        0          0   \n",
       "4                   0    3.0  1.000000      0       1        0          0   \n",
       "...               ...    ...       ...    ...     ...      ...        ...   \n",
       "2391                0   -3.0 -0.500000      0       0        1          0   \n",
       "2392                0    2.0  0.500000      0       0        1          0   \n",
       "2393                0    2.0  0.000000      0       1        0          0   \n",
       "2394                2    0.0  0.500000      0       0        0          1   \n",
       "2395                0   -3.0 -0.400000      0       0        1          0   \n",
       "\n",
       "      length  \n",
       "0         67  \n",
       "1         47  \n",
       "2         99  \n",
       "3         63  \n",
       "4         73  \n",
       "...      ...  \n",
       "2391     120  \n",
       "2392      18  \n",
       "2393      66  \n",
       "2394      82  \n",
       "2395      36  \n",
       "\n",
       "[2396 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words='english')\n",
    "train_tf_idf_features = vectorizer.fit_transform(train_data[\"traintxt\"]).toarray()\n",
    "train_tf_idf = pd.DataFrame(train_tf_idf_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      ],
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       "      0     1     2     3     4     5     6     7     8     9     ...  4596  \\\n",
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       "\n",
       "      4597  4598  4599  4600  4601  4602  4603  4604  4605  \n",
       "0      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
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       "2      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2391   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2392   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2393   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2394   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2395   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[2396 rows x 4606 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_tf_idf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Use BoW representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn import preprocessing\n",
    "\n",
    "comp_vectorizer = CountVectorizer(max_features=3000, stop_words=\"english\")\n",
    "train_bow_features = comp_vectorizer.fit_transform(train_data[\"traintxt\"]).toarray()\n",
    "train_bow = pd.DataFrame(train_bow_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      0     1     2     3     4     5     6     7     8     9     ...  2990  \\\n",
       "0        0     0     0     0     0     0     0     0     0     0  ...     0   \n",
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       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
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       "\n",
       "      2991  2992  2993  2994  2995  2996  2997  2998  2999  \n",
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       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
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       "\n",
       "[2396 rows x 3000 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_bow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Baseline Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF - RandomForestClassifier - baseline without cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # Consider class unbalanced\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.model_selection import GridSearchCV\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "\n",
    "# # 0.78, 6th submission\n",
    "# selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "#                      'number_of_quote', 'number_of_stop', 'number_of_mention',\n",
    "#                      'affin', 'blob', 'apple', 'google', 'microsoft']\n",
    "\n",
    "# train = pd.merge(train_tf_idf, train_data[selected_features], left_index=True, right_index=True)\n",
    "# train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2)\n",
    "\n",
    "# tfidf_rf_clf_model = RandomForestClassifier(n_estimators = 1000, min_samples_split = 10, bootstrap = True,\n",
    "#                                       max_depth = 100, random_state = 0, class_weight='balanced')\n",
    "\n",
    "# tfidf_rf_clf_model.fit(train_X, train_Y)\n",
    "# pred_Y = tfidf_rf_clf_model.predict(test_X)\n",
    "\n",
    "# print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "# print(\"\\nRandom Forest Classifier results:\")\n",
    "# print(classification_report(test_Y, pred_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BoW - RandomForestClassifier - baseline without cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =>  0.81875\n",
      "\n",
      "Random Forest Classifier results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.72      0.74      0.73        72\n",
      "     neutral       0.86      0.91      0.88       333\n",
      "    positive       0.71      0.49      0.58        75\n",
      "\n",
      "    accuracy                           0.82       480\n",
      "   macro avg       0.76      0.71      0.73       480\n",
      "weighted avg       0.81      0.82      0.81       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Consider class unbalanced\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "\n",
    "# 0.78, 6th submission\n",
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_stop', 'number_of_mention',\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft']\n",
    "\n",
    "train = pd.merge(train_bow, train_data[selected_features], left_index=True, right_index=True)\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2)\n",
    "\n",
    "bow_rf_clf_model = RandomForestClassifier(n_estimators = 1000, min_samples_split = 10, bootstrap = True,\n",
    "                                      max_depth = 100, random_state = 0, class_weight='balanced')\n",
    "\n",
    "bow_rf_clf_model.fit(train_X, train_Y)\n",
    "pred_Y = bow_rf_clf_model.predict(test_X)\n",
    "\n",
    "print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "print(\"\\nRandom Forest Classifier results:\")\n",
    "print(classification_report(test_Y, pred_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestClassifier - with cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
      "{'max_depth': 100, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Accuracy =>  0.80625\n",
      "\n",
      "Decision Tree Classifier results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.66      0.69      0.68        72\n",
      "     neutral       0.85      0.90      0.87       333\n",
      "    positive       0.74      0.49      0.59        75\n",
      "\n",
      "    accuracy                           0.81       480\n",
      "   macro avg       0.75      0.70      0.71       480\n",
      "weighted avg       0.80      0.81      0.80       480\n",
      "\n",
      "\n",
      "Best parameters: {'max_depth': 100, 'min_samples_split': 10, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [10, 50, 100],\n",
    "    'min_samples_split': [5, 10, 20],\n",
    "    'n_estimators': [100, 200, 500, 1000]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(class_weight='balanced')\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, \n",
    "                           cv = 3, n_jobs = -1, verbose = 2)\n",
    "\n",
    "grid_search.fit(train_X, train_Y)\n",
    "print(grid_search.best_params_)\n",
    "\n",
    "best_grid = grid_search.best_estimator_\n",
    "pred_Y = best_grid.predict(test_X)\n",
    "\n",
    "print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "print(\"\\nDecision Tree Classifier results:\")\n",
    "print(classification_report(test_Y, pred_Y))\n",
    "print('\\nBest parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BalancedRandomForestClassifier - without cross validation as baseline\n",
    "\n",
    "The idea of using this classifer comes from: https://stats.stackexchange.com/questions/242833/is-random-forest-a-good-option-for-unbalanced-data-classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =>  0.6583333333333333\n",
      "\n",
      "Random Forest Classifier results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.43      0.76      0.55        72\n",
      "     neutral       0.90      0.64      0.75       333\n",
      "    positive       0.43      0.65      0.52        75\n",
      "\n",
      "    accuracy                           0.66       480\n",
      "   macro avg       0.58      0.68      0.60       480\n",
      "weighted avg       0.75      0.66      0.68       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "\n",
    "clf = BalancedRandomForestClassifier(random_state=0)\n",
    "clf.fit(train_X, train_Y) \n",
    "pred_Y = clf.predict(test_X)\n",
    "\n",
    "print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "print(\"\\nRandom Forest Classifier results:\")\n",
    "print(classification_report(test_Y, pred_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF - MultinomialNB - with cross validation on alpha and do not consider the meta features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:509: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 0.42105263157894735 with accuracy: 0.7083333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn import *\n",
    "\n",
    "selected_features = []\n",
    "train = pd.merge(train_tf_idf, train_data[selected_features], left_index=True, right_index=True)\n",
    "\n",
    "N_a = 20\n",
    "cross = []\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2, random_state = 42)\n",
    "\n",
    "alphas = np.linspace(0, 2, N_a)\n",
    "for alp in alphas:\n",
    "    mmodel_tf = naive_bayes.MultinomialNB(alpha = alp)\n",
    "    mmodel_tf.fit(train_X, train_Y)\n",
    "\n",
    "    predY = mmodel_tf.predict(test_X)\n",
    "    acc = metrics.accuracy_score(test_Y, predY)\n",
    "    cross.append(acc)\n",
    "\n",
    "idx = np.argmax(cross)\n",
    "print(\"Best alpha:\", alphas[idx], 'with accuracy:', max(cross))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BoW - MultinomialNB - with cross validation on alpha and do not consider the meta features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:509: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 2.0 with accuracy: 0.7020833333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn import *\n",
    "\n",
    "selected_features = []\n",
    "train = pd.merge(train_bow, train_data[selected_features], left_index=True, right_index=True)\n",
    "\n",
    "N_a = 20\n",
    "cross = []\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2, random_state = 42)\n",
    "\n",
    "alphas = np.linspace(0, 2, N_a)\n",
    "for alp in alphas:\n",
    "    mmodel_tf = naive_bayes.MultinomialNB(alpha = alp)\n",
    "    mmodel_tf.fit(train_X, train_Y)\n",
    "\n",
    "    predY = mmodel_tf.predict(test_X)\n",
    "    acc = metrics.accuracy_score(test_Y, predY)\n",
    "    cross.append(acc)\n",
    "\n",
    "idx = np.argmax(cross)\n",
    "print(\"Best alpha:\", alphas[idx], 'with accuracy:', max(cross))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MultinomialNB - with cross validation on alpha and consider the meta features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:509: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 0.0 with accuracy: 0.68125\n"
     ]
    }
   ],
   "source": [
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_stop', 'number_of_mention', \n",
    "                     'apple', 'google', 'microsoft']\n",
    "\n",
    "train = pd.merge(train_tf_idf, train_data[selected_features], left_index=True, right_index=True)\n",
    "\n",
    "N_a = 20\n",
    "cross = []\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2, random_state = 42)\n",
    "\n",
    "alphas = np.linspace(0, 2, N_a)\n",
    "for alp in alphas:\n",
    "    mmodel_tf = naive_bayes.MultinomialNB(alpha = alp)\n",
    "    mmodel_tf.fit(train_X, train_Y)\n",
    "    \n",
    "    predY = mmodel_tf.predict(test_X)\n",
    "    acc = metrics.accuracy_score(test_Y, predY)\n",
    "    cross.append(acc)\n",
    "\n",
    "idx = np.argmax(cross)\n",
    "print(\"Best alpha:\", alphas[idx], 'with accuracy:', max(cross))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF - ExtraTreesClassifier - without cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =>  0.7666666666666667\n",
      "\n",
      "Extra Trees Classifier results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.91      0.41      0.56        79\n",
      "     neutral       0.75      0.98      0.85       325\n",
      "    positive       0.79      0.25      0.38        76\n",
      "\n",
      "    accuracy                           0.77       480\n",
      "   macro avg       0.82      0.54      0.60       480\n",
      "weighted avg       0.79      0.77      0.73       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# 0.72 - third submission\n",
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_mention',\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft', 'twitter']\n",
    "\n",
    "train = pd.merge(train_tf_idf, train_data[selected_features], left_index=True, right_index=True)\n",
    "\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2)\n",
    "clf_model = ExtraTreesClassifier(n_estimators = 1000, max_depth = 400, random_state = 100)\n",
    "\n",
    "clf_model.fit(train_X, train_Y)\n",
    "pred_Y = clf_model.predict(test_X)\n",
    "\n",
    "print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "print(\"\\nExtra Trees Classifier results:\")\n",
    "print(classification_report(test_Y, pred_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF - ExtraTreesClassifier - with cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
      "{'max_depth': 400, 'n_estimators': 500}\n",
      "Accuracy =>  0.74375\n",
      "\n",
      "Decision Tree Classifier results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.34      0.48        90\n",
      "     neutral       0.73      0.98      0.84       315\n",
      "    positive       0.95      0.24      0.38        75\n",
      "\n",
      "    accuracy                           0.74       480\n",
      "   macro avg       0.82      0.52      0.57       480\n",
      "weighted avg       0.78      0.74      0.70       480\n",
      "\n",
      "\n",
      "Best parameters: {'max_depth': 400, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_mention',\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft', 'twitter']\n",
    "\n",
    "train = pd.merge(train_tf_idf, train_data[selected_features], left_index=True, right_index=True)\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2)\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [50, 100, 200, 400],\n",
    "    'n_estimators': [100, 200, 500, 1000]\n",
    "}\n",
    "\n",
    "et = ExtraTreesClassifier(random_state = 100)\n",
    "grid_search = GridSearchCV(estimator = et, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)\n",
    "\n",
    "grid_search.fit(train_X, train_Y)\n",
    "print(grid_search.best_params_)\n",
    "\n",
    "best_grid = grid_search.best_estimator_\n",
    "pred_Y = best_grid.predict(test_X)\n",
    "\n",
    "print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "print(\"\\nDecision Tree Classifier results:\")\n",
    "print(classification_report(test_Y, pred_Y))\n",
    "print('\\nBest parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BoW - ExtraTreesClassifier - without cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =>  0.75625\n",
      "\n",
      "Extra Trees Classifier results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.89      0.46      0.60        85\n",
      "     neutral       0.75      0.96      0.84       318\n",
      "    positive       0.62      0.23      0.34        77\n",
      "\n",
      "    accuracy                           0.76       480\n",
      "   macro avg       0.75      0.55      0.60       480\n",
      "weighted avg       0.75      0.76      0.72       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_mention',\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft', 'twitter']\n",
    "\n",
    "train = pd.merge(train_bow, train_data[selected_features], left_index=True, right_index=True)\n",
    "\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2)\n",
    "clf_model = ExtraTreesClassifier(n_estimators = 1000, max_depth = 400, random_state = 100)\n",
    "\n",
    "clf_model.fit(train_X, train_Y)\n",
    "pred_Y = clf_model.predict(test_X)\n",
    "\n",
    "print(\"Accuracy => \", accuracy_score(pred_Y, test_Y))\n",
    "print(\"\\nExtra Trees Classifier results:\")\n",
    "print(classification_report(test_Y, pred_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding + LSTM\n",
    "\n",
    "The following codes are introduced by @yeedas and TensorFlow's official document:\n",
    "1. https://www.tensorflow.org/text/tutorials/classify_text_with_bert\n",
    "2. https://github.com/yeedas/Musicreviews/blob/master/AmazonMusicReviews_WordEmbeddings.ipynb\n",
    "\n",
    "I tried to mimic how they exploit embeddings and word2vector to build their classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "\n",
    "news_path = '/Users/saaries/Downloads/GoogleNews-vectors-negative300.bin'\n",
    "embeddings_index = KeyedVectors.load_word2vec_format(news_path, binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "\n",
    "def build_vocab(sentences, verbose =  True):\n",
    "    vocab = {}\n",
    "    for sentence in tqdm(sentences, disable = (not verbose)):\n",
    "        for word in sentence:\n",
    "            try:\n",
    "                vocab[word] += 1\n",
    "            except KeyError:\n",
    "                vocab[word] = 1\n",
    "    return vocab\n",
    "\n",
    "def remove_unwanted(string):\n",
    "    string = re.sub(\"http://[\\w|./]*\",\"\", string)\n",
    "    string = re.sub(\"@[\\w]*\",\"\", string)\n",
    "    string = re.sub(\"#[\\w]*\",\"\", string)\n",
    "    \n",
    "    string = re.sub(\"&lt;3\", \"love\", string)\n",
    "    string = re.sub(\"w/\", \"with \", string)\n",
    "    string = re.sub(\"'ve'\", \" have\", string)\n",
    "    string = re.sub(\"'ll\", \" will\", string)\n",
    "    string = re.sub(\"'re\", \" are\", string)\n",
    "    \n",
    "    string = re.sub(\"–\"  , \" \", string)\n",
    "    string = re.sub(\"’s\"  , \"'s\", string)\n",
    "    string = re.sub(\",\"  , \" \", string)\n",
    "    string = re.sub(\"\\.\" , \" \", string)\n",
    "    string = re.sub(\"-\"  , \" \", string)\n",
    "    string = re.sub(\"!\"  , \" \", string)\n",
    "    string = re.sub(\"\\|\" , \" \", string)\n",
    "    string = re.sub(\"\\\"\" , \" \", string)\n",
    "    string = re.sub(\":\"  , \" \", string)\n",
    "    string = re.sub(\"/\"  , \" \", string)\n",
    "    string = re.sub(\"#\"  , \" \", string)\n",
    "    \n",
    "    string = re.sub(\"\\”\"  , \" \", string)\n",
    "    string = re.sub(\"\\“\"  , \" \", string)\n",
    "    \n",
    "    string = re.sub(\"\\[\"  , \" \", string)\n",
    "    string = re.sub(\"\\]\"  , \" \", string)\n",
    "    \n",
    "    string = re.sub(\"\\(\"  , \" \", string)\n",
    "    string = re.sub(\"\\)\"  , \" \", string)\n",
    "    \n",
    "    string = re.sub(\"\\?\"  , \" \", string)\n",
    "    string = re.sub(\"\\;\"  , \" \", string)\n",
    "    \n",
    "    return string.strip().lower()\n",
    "\n",
    "def check_coverage(vocab,embeddings_index):\n",
    "    a = {}\n",
    "    oov = {}\n",
    "    k = 0\n",
    "    i = 0\n",
    "    for word in tqdm(vocab):\n",
    "        try:\n",
    "            a[word] = embeddings_index[word]\n",
    "            k += vocab[word]\n",
    "        except:\n",
    "            oov[word] = vocab[word]\n",
    "            i += vocab[word]\n",
    "            pass\n",
    "\n",
    "    print('Found embeddings for {:.2%} of vocab'.format(len(a) / len(vocab)))\n",
    "    print('Found embeddings for  {:.2%} of all text'.format(k / (k + i)))\n",
    "    sorted_x = sorted(oov.items(), key=operator.itemgetter(1))[::-1]\n",
    "    return sorted_x\n",
    "\n",
    "def clean_numbers(x):\n",
    "\n",
    "    x = re.sub('[0-9]{5,}', '#####', x)\n",
    "    x = re.sub('[0-9]{4}', '####', x)\n",
    "    x = re.sub('[0-9]{3}', '###', x)\n",
    "    x = re.sub('[0-9]{2}', '##', x)\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py:648: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n",
      "100%|██████████| 2396/2396 [00:00<00:00, 532313.81it/s]\n",
      "100%|██████████| 2396/2396 [00:00<00:00, 391429.17it/s]\n",
      "100%|██████████| 4705/4705 [00:00<00:00, 187018.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found embeddings for 87.44% of vocab\n",
      "Found embeddings for  95.96% of all text\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "c1 = np.array(traintxt)\n",
    "c2 = np.array(traintopic)\n",
    "c3 = np.array(trainY)\n",
    "\n",
    "dix = {\"traintxt\": c1,\n",
    "       \"traintopic\":c2,\n",
    "       \"trainY\":c3}\n",
    "\n",
    "train_data = pd.DataFrame(dix)\n",
    "\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(remove_unwanted)\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(lambda x: clean_numbers(x))\n",
    "\n",
    "to_remove = ['a','to','of','and']\n",
    "sentences = train_data[\"traintxt\"].apply(lambda x: x.split())\n",
    "\n",
    "sentences = [[word for word in sentence if not word in to_remove] for sentence in tqdm(sentences)]\n",
    "vocab = build_vocab(sentences)\n",
    "\n",
    "oov = check_coverage(vocab,embeddings_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4627 unique tokens.\n",
      "4628\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/keras_preprocessing/text.py:180: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
      "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "texts = []\n",
    "for txt in train_data[\"traintxt\"]:\n",
    "    texts.append(txt)\n",
    "    \n",
    "MAX_NB_WORDS = 40000\n",
    "MAX_SEQUENCE_LENGTH = 100\n",
    "\n",
    "tokenizer = Tokenizer(nb_words = MAX_NB_WORDS)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "sequences = tokenizer.texts_to_sequences(texts)\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "\n",
    "data = pad_sequences(sequences, maxlen = MAX_SEQUENCE_LENGTH)\n",
    "vocab = tokenizer.sequences_to_texts(texts)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print (vocab_size)\n",
    "embedding_matrix = np.zeros((vocab_size, 100))\n",
    "\n",
    "for word, i in tokenizer.word_index.items():\n",
    "    try:\n",
    "        embedding_vector = embeddings_ap[word]\n",
    "        if embedding_vector is not None:\n",
    "            embedding_matrix[i] = embedding_vector\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1916, 100)\n",
      "(1916, 3)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(trainY)\n",
    "y = to_categorical(np.asarray(y))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.2, random_state = 40)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 400000 word vectors.\n",
      "4628\n"
     ]
    }
   ],
   "source": [
    "embeddings_index = dict()\n",
    "\n",
    "f = open('/Users/saaries/Downloads/glove.6B.100d.txt')\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Loaded %s word vectors.' % len(embeddings_index))\n",
    "vocab = tokenizer.sequences_to_texts(texts)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print (vocab_size)\n",
    "\n",
    "embedding_matrix = np.zeros((vocab_size, 100))\n",
    "for word, i in tokenizer.word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "review_input (InputLayer)    [(None, 100)]             0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 100, 100)          462800    \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 10000)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               1000100   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                3232      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 99        \n",
      "=================================================================\n",
      "Total params: 1,466,231\n",
      "Trainable params: 1,466,231\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential,Model\n",
    "from keras.layers import Dense,Dropout,Activation\n",
    "from keras.layers import Flatten,Input\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import concatenate\n",
    "\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "# Add sequential model\n",
    "sentiment_model = Sequential()\n",
    "\n",
    "# Add embedding layer \n",
    "Embed_Layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=(MAX_SEQUENCE_LENGTH,), trainable=True)\n",
    "\n",
    "#define Inputs\n",
    "review_input = Input(shape=(MAX_SEQUENCE_LENGTH,),dtype= 'int32',name = 'review_input')\n",
    "review_embedding = Embed_Layer(review_input)\n",
    "Flatten_Layer = Flatten()\n",
    "review_flatten = Flatten_Layer(review_embedding)\n",
    "output_size = 3\n",
    "\n",
    "dense1 = Dense(100,activation='relu')(review_flatten)\n",
    "\n",
    "dense2 = Dense(32,activation='relu')(dense1)\n",
    "predict = Dense(3,activation='softmax')(dense2)\n",
    "\n",
    "sentiment_model = Model(inputs=[review_input],outputs=[predict])\n",
    "sentiment_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])\n",
    "\n",
    "print(sentiment_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-44-64b0e4fc3c84>:3: experimental_run_functions_eagerly (from tensorflow.python.eager.def_function) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.run_functions_eagerly` instead of the experimental version.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py:4212: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
      "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.8346 - acc: 0.6623 - val_loss: 0.7685 - val_acc: 0.6833\n",
      "Epoch 2/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.6458 - acc: 0.7281 - val_loss: 0.7413 - val_acc: 0.6729\n",
      "Epoch 3/10\n",
      "60/60 [==============================] - 1s 18ms/step - loss: 0.4403 - acc: 0.8325 - val_loss: 0.7731 - val_acc: 0.7167\n",
      "Epoch 4/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.2202 - acc: 0.9374 - val_loss: 0.8364 - val_acc: 0.6958\n",
      "Epoch 5/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.1043 - acc: 0.9765 - val_loss: 1.0026 - val_acc: 0.7000\n",
      "Epoch 6/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.0489 - acc: 0.9880 - val_loss: 1.0747 - val_acc: 0.7042\n",
      "Epoch 7/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.0345 - acc: 0.9937 - val_loss: 1.2652 - val_acc: 0.6958\n",
      "Epoch 8/10\n",
      "60/60 [==============================] - 1s 18ms/step - loss: 0.0367 - acc: 0.9937 - val_loss: 1.2617 - val_acc: 0.6854\n",
      "Epoch 9/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.0220 - acc: 0.9963 - val_loss: 1.3044 - val_acc: 0.6896\n",
      "Epoch 10/10\n",
      "60/60 [==============================] - 1s 19ms/step - loss: 0.0161 - acc: 0.9963 - val_loss: 1.3119 - val_acc: 0.6917\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbef3da2650>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.config.experimental_run_functions_eagerly(True)\n",
    "sentiment_model.fit(X_train,y_train,epochs = 10,batch_size = 32,verbose = True,validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = np.array(testtxt)\n",
    "dix = {\"txt\": c1}\n",
    "\n",
    "test_data = pd.DataFrame(dix)\n",
    "\n",
    "test_data[\"txt\"] = test_data[\"txt\"].apply(remove_unwanted)\n",
    "test_data[\"txt\"] = test_data[\"txt\"].apply(lambda x: clean_numbers(x))\n",
    "\n",
    "text_txts = []\n",
    "for txt in test_data[\"txt\"]:\n",
    "    text_txts.append(txt)\n",
    "    \n",
    "sequences = tokenizer.texts_to_sequences(text_txts)\n",
    "test_data = pad_sequences(sequences, maxlen = MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "predictions = sentiment_model.predict(test_data)\n",
    "\n",
    "pred = np.argmax(predictions, axis=1)\n",
    "result = []\n",
    "\n",
    "for i in range(len(pred)):\n",
    "    if pred[i] == 0:\n",
    "        result.append('negative')\n",
    "    if pred[i] == 1:\n",
    "        result.append('neutral')\n",
    "    if pred[i] == 2:\n",
    "        result.append('positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_csv_kaggle_sub(\"LSTM_submission.csv\", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier Fine Tuned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForest\n",
    "Notes: I am more familiar with the RandomForest classifier. In view of its preferable performance among the above baseline classifiers, I decided to fine tune it.\n",
    "\n",
    "1. I used cross_val_score() to evaluate the parameters.\n",
    "\n",
    "2. I firstly evaluate the @n_estimators, which is one of the most important params in RandomForestClassifier and decides the number of trees in the forest. We select the most informative model by maximize the accuracy over this parameter.\n",
    "\n",
    "3. Then, I evaluate the @max_depth, aiming to reduce the forest's complexity (reduce the maximum tree height of trees in the forest).\n",
    "\n",
    "However, this fine-tuning did not work as well as I excepted.\n",
    "I also tried different combination of features, selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_stop', 'number_of_mention', ''\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft'] is the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# testtxt\n",
    "c1 = np.array(traintxt)\n",
    "dix = {\"traintxt\": c1}\n",
    "test_data = pd.DataFrame(dix)\n",
    "\n",
    "add_extra_feature(train_data, train_data[\"traintxt\"])\n",
    "\n",
    "train_data['affin'] = train_data['traintxt'].apply(eva_afinn_score)\n",
    "train_data['blob'] = train_data['traintxt'].apply(eva_blob_score)\n",
    "train_data['traintxt'] = train_data['traintxt'].apply(clean_str)\n",
    "\n",
    "eva_comp(train_data, train_data[\"traintxt\"])\n",
    "train_data['length'] =  train_data[\"traintxt\"].apply(lambda x: (len(x)))\n",
    "\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(tokenize)\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(stemming)\n",
    "train_data[\"traintxt\"] = train_data[\"traintxt\"].apply(word_to_sen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy =  0.7369436636070925  is achieved by n_estimators =  1000\n"
     ]
    },
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   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_stop', 'number_of_mention', ''\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft']\n",
    "\n",
    "train = pd.merge(train_tf_idf, train_data[selected_features], left_index=True, right_index=True)\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(train, trainY, test_size=0.2)\n",
    "\n",
    "score_lt = []\n",
    "forest = np.arange(900, 1200, 50)\n",
    "\n",
    "for i in forest:\n",
    "    rfc = RandomForestClassifier(n_estimators = i ,random_state = 0)\n",
    "    score = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "    score_lt.append(score)\n",
    "    \n",
    "idx = argmax(score_lt)\n",
    "best_n = forest[idx]\n",
    "print('Best accuracy = ', max(score_lt), ' is achieved by n_estimators = ', best_n)\n",
    "\n",
    "plt.plot(forest, score_lt, 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy =  0.7374653120160648  is achieved by max_depth =  170\n"
     ]
    },
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   "source": [
    "score_lt = []\n",
    "forest = np.arange(10, 200, 20)\n",
    "\n",
    "for i in forest:\n",
    "    rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = i)\n",
    "    score = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "    score_lt.append(score)\n",
    "    \n",
    "idx = argmax(score_lt)\n",
    "best_d = forest[idx]\n",
    "print('Best accuracy = ', max(score_lt), ' is achieved by max_depth = ', best_d)\n",
    "\n",
    "plt.plot(forest, score_lt, 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy =  0.7374653120160648  is achieved by min_samples_split =  2\n"
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   "source": [
    "score_lt = []\n",
    "forest = np.arange(2, 32, 5)\n",
    "\n",
    "for i in forest:\n",
    "    rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = best_d,\n",
    "                                 min_samples_split = i)\n",
    "    score = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "    score_lt.append(score)\n",
    "    \n",
    "idx = argmax(score_lt)\n",
    "best_mss = forest[idx]\n",
    "print('Best accuracy = ', max(score_lt), ' is achieved by min_samples_split = ', best_mss)\n",
    "\n",
    "plt.plot(forest, score_lt, 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy =  0.7004135576257965  is achieved by min_samples_leaf =  2\n"
     ]
    },
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   "source": [
    "score_lt = []\n",
    "forest = np.arange(2, 32, 3)\n",
    "\n",
    "for i in forest:\n",
    "    rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = best_d,\n",
    "                                 min_samples_split = best_mss, min_samples_leaf = i)\n",
    "    score = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "    score_lt.append(score)\n",
    "    \n",
    "idx = argmax(score_lt)\n",
    "best_msl = forest[idx]\n",
    "print('Best accuracy = ', max(score_lt), ' is achieved by min_samples_leaf = ', best_msl)\n",
    "\n",
    "plt.plot(forest, score_lt, 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with bootstrap = True :  0.7004135576257965\n",
      "Accuracy with bootstrap = False :  0.7249392091548478\n"
     ]
    }
   ],
   "source": [
    "score_lt = []\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = best_d,\n",
    "                             min_samples_split = best_mss, min_samples_leaf = best_msl,\n",
    "                             bootstrap = True)\n",
    "score_T = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "print('Accuracy with bootstrap = True : ', score_T)\n",
    "\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = best_d,\n",
    "                             min_samples_split = best_mss, min_samples_leaf = best_msl,\n",
    "                             bootstrap = False)\n",
    "score_F = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "print('Accuracy with bootstrap = False : ', score_F)\n",
    "\n",
    "best_bs = score_T > score_F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with class_weight = balanced :  0.6946737898656306\n",
      "Accuracy with class_weight = balanced_subsample :  0.6946737898656306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    }
   ],
   "source": [
    "score_lt = []\n",
    "forest = ['balanced', 'balanced_subsample']\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = best_d,\n",
    "                             min_samples_split = best_mss, min_samples_leaf = best_msl,\n",
    "                             bootstrap = best_bs, class_weight = 'balanced')\n",
    "score_T = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "print('Accuracy with class_weight = balanced : ', score_T)\n",
    "\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = best_n ,random_state = 0, max_depth = best_d,\n",
    "                             min_samples_split = best_mss, min_samples_leaf = best_msl,\n",
    "                             bootstrap = best_bs, class_weight = 'balanced_subsample')\n",
    "score_F = cross_val_score(rfc, train_X, train_Y, cv = 3).mean()\n",
    "print('Accuracy with class_weight = balanced_subsample : ', score_F)\n",
    "\n",
    "best_cw = forest[score_T < score_F]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write data\n",
    "### Write the best classifier's prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# testtxt\n",
    "c1 = np.array(testtxt)\n",
    "dix = {\"testtxt\": c1}\n",
    "test_data = pd.DataFrame(dix)\n",
    "\n",
    "add_extra_feature(test_data, test_data[\"testtxt\"])\n",
    "\n",
    "test_data['affin'] = test_data['testtxt'].apply(eva_afinn_score)\n",
    "test_data['blob'] = test_data['testtxt'].apply(eva_blob_score)\n",
    "test_data['traintxt'] = test_data['testtxt'].apply(clean_str)\n",
    "\n",
    "eva_comp(test_data, test_data[\"testtxt\"])\n",
    "test_data['length'] =  test_data[\"testtxt\"].apply(lambda x: (len(x)))\n",
    "\n",
    "test_data[\"testtxt\"] = test_data[\"testtxt\"].apply(tokenize)\n",
    "# test_data[\"testtxt\"] = test_data[\"traintxt\"].apply(tokenize)\n",
    "test_data[\"testtxt\"] = test_data[\"testtxt\"].apply(stemming)\n",
    "test_data[\"testtxt\"] = test_data[\"testtxt\"].apply(word_to_sen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>does anyone out there have some good online tr...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td># googl announc # nfc-base # android beam for ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>google announces  nfc based  android beam for ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>samsung e o googl lançam hoje o galaxi nexu e ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>samsung e o google lan am hoje o galaxy nexus ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td># microsoft eye A new approach to social # sea...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.084848</td>\n",
       "      <td>microsoft eyes a new approach to social  search</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>PC podcast 39 ( # gitex , # microsoft # surfac...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>pc podcast      gitex   microsoft  surface    ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1028 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                testtxt  number_of_quote  \\\n",
       "0     what do we have to do to get flash on # ipad ?...              0.0   \n",
       "1     RT @ ruilifestyl : # googl the mixtap http : /...              0.0   \n",
       "2     appl to orang mayb , but # twitter is way more...              0.0   \n",
       "3     thank @ appl for an OS updat that ha onli succ...              0.0   \n",
       "4                            wht goin ON # twitter fam               0.0   \n",
       "...                                                 ...              ...   \n",
       "1023  doe anyon out there have some good onlin train...              0.0   \n",
       "1024  # googl announc # nfc-base # android beam for ...              0.0   \n",
       "1025  samsung e o googl lançam hoje o galaxi nexu e ...              0.0   \n",
       "1026  # microsoft eye A new approach to social # sea...              0.0   \n",
       "1027  PC podcast 39 ( # gitex , # microsoft # surfac...              0.0   \n",
       "\n",
       "      number_of_stop  number_of_exclamation  number_of_questionmark  \\\n",
       "0                135                      1                       1   \n",
       "1                 58                      0                       0   \n",
       "2                 92                      0                       0   \n",
       "3                 73                      0                       0   \n",
       "4                 25                      0                       0   \n",
       "...              ...                    ...                     ...   \n",
       "1023             135                      0                       1   \n",
       "1024             114                      0                       0   \n",
       "1025              99                      0                       0   \n",
       "1026              69                      0                       0   \n",
       "1027             137                      0                       0   \n",
       "\n",
       "      number_of_hashtag  number_of_mention  number_of_comma  affin      blob  \\\n",
       "0                     1                  3                0    5.0  0.000000   \n",
       "1                     1                  1                0    0.0  0.000000   \n",
       "2                     2                  0                1    0.0  0.450000   \n",
       "3                     0                  1                0    2.0  0.100000   \n",
       "4                     1                  0                0    0.0  0.000000   \n",
       "...                 ...                ...              ...    ...       ...   \n",
       "1023                  2                  0                0    3.0  0.700000   \n",
       "1024                  5                  0                0    0.0  0.000000   \n",
       "1025                  2                  0                0    0.0  0.000000   \n",
       "1026                  2                  0                0    0.0  0.084848   \n",
       "1027                  3                  1                1    0.0  0.500000   \n",
       "\n",
       "                                               traintxt  apple  google  \\\n",
       "0     what do we have to do to get flash on  ipad   ...      0       0   \n",
       "1                 rt  ruilifestyle   google the mixtape      0       0   \n",
       "2     apples to oranges maybe  but  twitter is way m...      0       0   \n",
       "3     thanks  apple for an os update that has only s...      1       0   \n",
       "4                             whts goin on  twitter fam      0       0   \n",
       "...                                                 ...    ...     ...   \n",
       "1023  does anyone out there have some good online tr...      0       0   \n",
       "1024  google announces  nfc based  android beam for ...      0       0   \n",
       "1025  samsung e o google lan am hoje o galaxy nexus ...      0       1   \n",
       "1026    microsoft eyes a new approach to social  search      0       0   \n",
       "1027  pc podcast      gitex   microsoft  surface    ...      0       0   \n",
       "\n",
       "      twitter  microsoft  length  \n",
       "0           0          0     135  \n",
       "1           0          0      58  \n",
       "2           0          0      92  \n",
       "3           0          0      73  \n",
       "4           0          0      25  \n",
       "...       ...        ...     ...  \n",
       "1023        0          0     135  \n",
       "1024        0          0     114  \n",
       "1025        0          0      99  \n",
       "1026        0          0      69  \n",
       "1027        0          0     138  \n",
       "\n",
       "[1028 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_tf_idf_features = vectorizer.transform(test_data[\"testtxt\"]).toarray()\n",
    "# test_tf_idf = pd.DataFrame(test_tf_idf_features)\n",
    "\n",
    "test_bow_features = comp_vectorizer.transform(test_data[\"testtxt\"]).toarray()\n",
    "test_bow = pd.DataFrame(test_bow_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['neutral' 'neutral' 'neutral' ... 'neutral' 'neutral' 'neutral']\n",
      "1028\n"
     ]
    }
   ],
   "source": [
    "selected_features = ['number_of_exclamation', 'number_of_questionmark', 'number_of_hashtag',\n",
    "                     'number_of_quote', 'number_of_stop', 'number_of_mention', ''\n",
    "                     'affin', 'blob', 'apple', 'google', 'microsoft']\n",
    "\n",
    "# test = pd.merge(test_tf_idf, test_data[selected_features], left_index=True, right_index=True)\n",
    "# pred_Y = tfidf_rf_clf_model.predict(test)\n",
    "\n",
    "test = pd.merge(test_bow, test_data[selected_features], left_index=True, right_index=True)\n",
    "pred_Y = bow_rf_clf_model.predict(test)\n",
    "\n",
    "print(pred_Y)\n",
    "print(len(pred_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_csv_kaggle_sub(\"BoW_submission.csv\", pred_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Closing Remarks\n",
    "\n",
    "**The best performance my model can achieve is balanced RandomForest classifier + meta features + BoW (This combination actually surprise me). It gives me the following hints:**\n",
    "\n",
    "1. BoW can outperform TFIDF\n",
    "2. Data pre-processing and cleansing is very important\n",
    "3. Analyae the database before taking any actions\n",
    "4. Meta features are very helpful, try your best to make use of it\n",
    "5. The LSTM NN I trained above seems overfitted, it acts poor on the test data\n",
    "\n",
    "**Before doing this assignment, I alse found some useful blogs that verified my ideas:**\n",
    "1. How to use BERT and Google's word embedding: https://www.kaggle.com/gunesevitan/nlp-with-disaster-tweets-eda-cleaning-and-bert#6.-Cross-validation\n",
    "2. How to analyse your database: https://towardsdatascience.com/text-analysis-feature-engineering-with-nlp-502d6ea9225d\n",
    "3. Feature Engineering (meta feature extraction, fantastic): https://www.analyticsvidhya.com/blog/2021/04/a-guide-to-feature-engineering-in-nlp/\n",
    "4. How to analyse your database: https://www.analyticsvidhya.com/blog/2021/06/twitter-sentiment-analysis-a-nlp-use-case-for-beginners/\n",
    "\n",
    "**My special tricks summary:**\n",
    "1. Handle special emoji. For example, '&lt;3' is the HTML code for a heart and suggests positive sentiment\n",
    "2. Extract meta features in punctuations. It's intuitive that people will use '!!!', '....', '???' to express special sentiments\n",
    "3. Make use of public sentiment analysis tools like Afinn and TextBlob\n",
    "4. Be careful about the class unbalance will be really helpful (raise 2% acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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