Hate_Speech_Detection_with_Machine_Learning(_Decsion_Tree)_(1).ipynb

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      "cell_type": "markdown",
      "source": [
        "#📚**Table Of Content**"
      ],
      "metadata": {
        "id": "sJ5d68_aEqL6"
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Introduction\n",
        "2.   import Libraries\n",
        "3.   Data Preprocessing\n",
        "4.   Data Spliting\n",
        "5.   Model training\n",
        "6.   Model testing\n",
        "\n"
      ],
      "metadata": {
        "id": "wNBKdwQ_Ey2F"
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    {
      "cell_type": "markdown",
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        "id": "3y5steRGZdan"
      },
      "source": [
        "# **📚Introduction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UuO8V2LFc_S3"
      },
      "source": [
        "Hate speech is one of the serious issues we see on social media platforms like Twitter and Facebook daily. Most of the posts containing hate speech can be found in the accounts of people with political views. So, if you want to learn how to train a hate speech detection model with machine learning, this article is for you. In this article, I will walk you through the task of hate speech detection with machine learning using Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eUj-SCYjLIG"
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      "source": [
        "There is no legal definition of hate speech because people’s opinions cannot easily be classified as hateful or offensive. Nevertheless, the United Nations defines hate speech as any type of verbal, written or behavioural communication that can attack or use discriminatory language regarding a person or a group of people based on their identity based on religion, ethnicity, nationality, race, colour, ancestry, gender or any other identity factor.\n",
        "\n",
        "Hope you now have understood what hate speech is. Social media platforms need to detect hate speech and prevent it from going viral or ban it at the right time. So in the section below, I will walk you through the task of hate speech detection with machine learning using the Python programming language."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📚 **Import Libraries**"
      ],
      "metadata": {
        "id": "1NS8hluSFGmC"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaqsrot6jqtj"
      },
      "source": [
        "# **Hate Speech Detection using Python**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAnXlGdJjxih"
      },
      "source": [
        "The dataset I’m using for the hate speech detection task is downloaded from Kaggle. This dataset was originally collected from Twitter and contains the following columns:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70b4YGjNj37A"
      },
      "source": [
        "- index\n",
        "- count\n",
        "- hate_speech\n",
        "- offensive_language\n",
        "- neither\n",
        "- class\n",
        "- tweet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0p34fjKlFoL"
      },
      "source": [
        "!pip install nltk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "NLTK, or Natural Language Toolkit, is a comprehensive library in Python for natural language processing (NLP) tasks. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. Some key functionalities of NLTK include:"
      ],
      "metadata": {
        "id": "JgRCOl9fFsPr"
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      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8vQ__OMkI9l",
        "outputId": "01b13bea-05cc-42d2-d0ad-ac85f77236ad"
      },
      "source": [
        "from nltk.util import pr\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import re\n",
        "import nltk\n",
        "stemmer = nltk.SnowballStemmer(\"english\")\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "nltk.download('stopwords')\n",
        "stopword=set(stopwords.words('english'))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wfEAwcpZkb1"
      },
      "source": [
        "# **📚Data Loading**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbcqNzNhZoxG",
        "outputId": "424d9c9f-5d8e-414f-eb00-f44e2a4e7c90"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8Mtkw7WktFL",
        "outputId": "319616a0-3c81-4b7a-d9b0-b54f81bf69ba"
      },
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Datasets (1)/Hate Speech/twitter.csv\")\n",
        "print(data.head())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0  count  hate_speech  offensive_language  neither  class  \\\n",
            "0           0      3            0                   0        3      2   \n",
            "1           1      3            0                   3        0      1   \n",
            "2           2      3            0                   3        0      1   \n",
            "3           3      3            0                   2        1      1   \n",
            "4           4      6            0                   6        0      1   \n",
            "\n",
            "                                               tweet  \n",
            "0  !!! RT @mayasolovely: As a woman you shouldn't...  \n",
            "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...  \n",
            "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...  \n",
            "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
            "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Data Preprocessing**"
      ],
      "metadata": {
        "id": "mZ6dM1VwGXnh"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCdF4Ub7eGSk"
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      "source": [
        "Now I will create a function to clean the texts in the tweet column:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNjgOMQXeIHF"
      },
      "source": [
        "def clean(text):\n",
        "    text = str(text).lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    text = [word for word in text.split(' ') if word not in stopword]\n",
        "    text=\" \".join(text)\n",
        "    text = [stemmer.stem(word) for word in text.split(' ')]\n",
        "    text=\" \".join(text)\n",
        "    return text\n",
        "data[\"tweet\"] = data[\"tweet\"].apply(clean)"
      ],
      "execution_count": 6,
      "outputs": []
    },
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        "id": "qgqTwJdMeimM",
        "outputId": "433d7380-4df8-4e17-be8d-10ed46d4b960"
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      "source": [
        "data"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 24783,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7299,\n        \"min\": 0,\n        \"max\": 25296,\n        \"num_unique_values\": 24783,\n        \"samples\": [\n          2326,\n          16283,\n          19362\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6,\n          7,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hate_speech\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"offensive_language\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neither\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24586,\n        \"samples\": [\n          \" happi birthday dirti littl bird  tear \",\n          \"lmfaoooooooooooooooooo bbbrook pussi good mama  like guess gotta  mama\",\n          \"rt satanspearl shes model instagram shes hoe town\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "# **📚Data Spliting**"
      ],
      "metadata": {
        "id": "RnPFSCUnJDrK"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1byM2qz4eSXt"
      },
      "source": [
        "Now let’s split the dataset into training and test sets and train a machine learning model for the task of hate speech detection:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QnnHBg8eUe0"
      },
      "source": [
        "x = np.array(data[\"tweet\"])\n",
        "y = np.array(data[\"hate_speech\"])\n",
        "\n",
        "cv = CountVectorizer()\n",
        "X = cv.fit_transform(x) # Fit the Data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Model Training**"
      ],
      "metadata": {
        "id": "n_kDml8GJYL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "TMOWOfSlJfGo",
        "outputId": "c8fcbfe2-be7b-4477-f6d5-f38d184ccf03"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Model testing**"
      ],
      "metadata": {
        "id": "EFWL4O7_JvVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_DT = clf.predict(X_test)\n",
        "DT_Acc=clf.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(clf.score(X_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NAQYKe0KRYt",
        "outputId": "cf94849b-137e-4048-c364-c4016d2536ba"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.7409\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mJxGUaIfYP4"
      },
      "source": [
        "Now let’s test this machine learning model to see if it detects hate speech or not:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_kcbORkfaAy",
        "outputId": "e187fceb-b014-467a-8b04-8053a6080da5"
      },
      "source": [
        "sample = \"Let's unite and kill all the people who are protesting against the government\"\n",
        "data = cv.transform([sample]).toarray()\n",
        "print(clf.predict(data))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCZnlXXpfvCz"
      },
      "source": [
        "data\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wb-f7U_ATlKy"
      },
      "source": [
        "# **References**\n",
        "[Hate Speech Detection with Machine Learning](https://thecleverprogrammer.com/2021/07/25/hate-speech-detection-with-machine-learning/)"
      ]
    }
  ]
}