[Ly]_BERTopic.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2pOAnJIwyMqn"
      },
      "source": [
        "#**Topic Modeling with BERT**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4JT5VtTyjkx"
      },
      "source": [
        "BERTopic is a topic modeling using transformers and c-TF-IDF to classify data to cluster (unsupervised)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c02IebSGyzb-"
      },
      "source": [
        "##**1. Architecture**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_LY0RuxUy_oX"
      },
      "source": [
        "![link text](https://maartengr.github.io/BERTopic/tutorial/algorithm/algorithm.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1p24N7n3OWy"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTxL4DI_zcCK"
      },
      "source": [
        "##**2. Environment**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-kV2kAJzr66"
      },
      "source": [
        "We install some libraries written to leverage BERTopic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTE7voQryS-u",
        "cellView": "form"
      },
      "source": [
        "#@title Install BERTopic, nltk, gensim\n",
        "!pip install bertopic\n",
        "!pip install nltk\n",
        "!pip install gensim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h17T7neJBBlH",
        "cellView": "form"
      },
      "source": [
        "#@title Import BERTopic, nltk, gensim and other libraries\n",
        "from bertopic import BERTopic\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "from nltk import word_tokenize, pos_tag\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from gensim.models import Phrases\n",
        "from gensim.parsing.preprocessing import remove_stopwords, preprocess_string\n",
        "import pathlib\n",
        "import logging\n",
        "from gensim import models, corpora\n",
        "from gensim.models import Phrases, LdaModel\n",
        "from gensim.test.utils import datapath\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnZCcaMj1Sx6"
      },
      "source": [
        "Download English data that is implemented with LDA algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvscFnO6zq4D",
        "cellView": "form"
      },
      "source": [
        "#@title Download dataset for train and test\n",
        "!wget https://github.com/hoavt-54/LDA-Demo/raw/master/data/wiki.train.tokens\n",
        "!wget https://github.com/hoavt-54/LDA-Demo/raw/master/data/wiki.test.tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1R7frUt18Gi"
      },
      "source": [
        "Import libraries for processing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJuEr3Z88i5y",
        "cellView": "form"
      },
      "source": [
        "#@title Import utils.py\n",
        "STOPW = {\"unk\", \"<unk>\"}\n",
        "wnl = WordNetLemmatizer()\n",
        "\n",
        "\n",
        "def extract_pos(doc, tag = [\"NN\"]):\n",
        "    text = word_tokenize(doc)\n",
        "    return \" \".join([t[0] for t in pos_tag(text) if t[1] in tag])\n",
        "\n",
        "\n",
        "def extract_bigrams(docs, biagram_model=None):\n",
        "    \"\"\"Extract bigrams features before POS remove\n",
        "    to keep interesting patterns\n",
        "    \"\"\"\n",
        "    list_tokens = [lemmatize(remove_stopw(doc.lower())).split() for doc in docs]\n",
        "    if not biagram_model:\n",
        "        biagram_model = Phrases(list_tokens, min_count=12, max_vocab_size=50000, threshold=3)\n",
        "    return (biagram_model, [[b for b in biagram_model[tks] if \"_\" in b] for tks in list_tokens])\n",
        "\n",
        "\n",
        "def lemmatize(doc):\n",
        "    return \" \".join([wnl.lemmatize(token) for token in doc.split()])\n",
        "\n",
        "\n",
        "def remove_non_word(string):\n",
        "    pattern = \"^[\\\\w-]+$\"\n",
        "    return \" \".join([token for token in string.split() if re.match(pattern, token)])\n",
        "\n",
        "\n",
        "def remove_stopw(string):\n",
        "    string = remove_stopwords(string)\n",
        "    return \" \".join([token for token in string.split() if token not in STOPW])\n",
        "\n",
        "\n",
        "def read_text_file(fn=\"test.txt\"):\n",
        "    with open(f\"data/{fn}\") as f:\n",
        "        return f.read()\n"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCCSx4OhujBi"
      },
      "source": [
        "Convert dataset to list of documents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "rBqnigtWPt_2"
      },
      "source": [
        "#@title dataset to documents\n",
        "def read_wiki(dataset=\"wiki.test.tokens\"):\n",
        "    \"\"\" In this dataset, document are separated by their title\n",
        "        having format = Title here =. We split the dataset set file\n",
        "        into chunks of text by the titles.\n",
        "    \"\"\"\n",
        "    \n",
        "    article = []\n",
        "    begin_pattern = \"^\\\\s*= [^=]\"  # Ex. = Robert <unk> = \n",
        "    with open(f\"{dataset}\") as f:\n",
        "        for line in f:\n",
        "            line = line.strip()\n",
        "            if not line:\n",
        "                continue\n",
        "            if re.match(begin_pattern, line):\n",
        "                if len(article) > 0:\n",
        "                    out = \" \".join(article)\n",
        "                    article = []\n",
        "                    yield out\n",
        "                else:\n",
        "                    article.append(line.strip())\n",
        "            else:\n",
        "                article.append(line.strip())\n",
        "        yield \" \".join(article)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8oJn9CkR6Qlo"
      },
      "source": [
        "Clean data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKtxLh9LAs6C",
        "cellView": "form"
      },
      "source": [
        "#@title Function for preprocessing data\n",
        "def preprocess(data, bigrams=None):\n",
        "    \"\"\"removing stopwords, filter part-of-speech\n",
        "        tokenization ...\n",
        "    \n",
        "    \"\"\"\n",
        "    data = [remove_non_word(doc) for doc in data]\n",
        "    print(f\"number of doc: {len(data)}\")\n",
        "    bigrams, bi_data = extract_bigrams(data)\n",
        "    data = [extract_pos(doc) for doc in data]\n",
        "    data = [doc for doc in data if 40 < len(doc) < 60000]\n",
        "    data = [remove_stopw(doc.lower()) for doc in data]\n",
        "    data = [lemmatize(doc) for doc in data]\n",
        "\n",
        "    return data\n",
        "\n",
        "    # list_of_tokens = [doc.split() for doc in data]\n",
        "    \n",
        "    # # concat bigrams\n",
        "    # for idx, tokens in enumerate(list_of_tokens):\n",
        "    #     tokens.extend(bi_data[idx])\n",
        "\n",
        "    # return (list_of_tokens, bigrams)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSOjy7ty3NBS"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouDzZ2-u3Ikn"
      },
      "source": [
        "##**3. Running**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brqtvcurvV8g"
      },
      "source": [
        "###**3.1. Default parameters**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZqdNOL4vbdj"
      },
      "source": [
        "Load dataset from file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxIcslSLvU4H"
      },
      "source": [
        "data_train = read_wiki(\"wiki.train.tokens\")\n",
        "docs = list(data_train)"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyJfLe_tvuG0"
      },
      "source": [
        "Example from train dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5Qe3VFH_aWQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "b50750a8-adaa-44ce-a8af-ddbfdc5418fa"
      },
      "source": [
        "docs[2]"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<unk> Mary Barker ( 28 June 1895 – 16 February 1973 ) was an English illustrator best known for a series of fantasy illustrations depicting fairies and flowers . Barker \\'s art education began in girlhood with correspondence courses and instruction at the Croydon School of Art . Her earliest professional work included <unk> cards and juvenile magazine illustrations , and her first book , Flower Fairies of the Spring , was published in 1923 . Similar books were published in the following decades . Barker was a devout Anglican , and donated her artworks to Christian fundraisers and missionary organizations . She produced a few Christian @-@ themed books such as The Children ’ s Book of Hymns and , in collaboration with her sister Dorothy , He Leadeth Me . She designed a stained glass window for St. Edmund \\'s Church , Pitlake , and her painting of the Christ Child , The Darling of the World Has Come , was purchased by Queen Mary . Barker was equally proficient in <unk> , pen and ink , oils , and <unk> . Kate Greenaway and the Pre @-@ <unk> were the principal influences on her work . She claimed to paint instinctively and rejected artistic theories . Barker died in 1973 . Though she published Flower Fairy books with spring , summer , and autumn themes , it wasn \\'t until 1985 that a winter collection was assembled from her remaining work and published posthumously . = = Biography = = = = = Early life = = = Barker was born the second daughter and youngest child of Walter Barker , a partner in a seed supply company and an amateur artist , and his wife Mary Eleanor ( Oswald ) Barker on 28 June 1895 at home at 66 Waddon Road in Croydon , Surrey , England . Barker was an <unk> as a child , and cared for at home by her parents . Later , her sister and elder by two years , Dorothy Oswald Barker , continued the care . The family of four was moderately well off , and belonged to the lower end of the upper middle class . A <unk> , a governess , and a cook to prepare special meals for Barker were hired . She spent much time in bed at home amusing herself with painting books and a nursery library that included the works of Kate Greenaway and Randolph Caldecott – two artists who exerted strong influences on her later art . = = = Art education and first professional work = = = Barker took correspondence courses in art , probably until about 1919 . In 1908 at 13 years , she entered an evening class at the Croydon School of Art , and attended the school into the 1940s . In time , she received a teaching position . In 1911 , Raphael Tuck & Sons bought four of Barker \\'s \" little drawings \" for half a sovereign , and published them as postcards . In October 1911 , she won second prize in the Croydon Art Society \\'s poster competition , and shortly afterward was elected the youngest member of the Society . The art critic for the Croydon Advertiser remarked , \" Her drawings show a remarkable freedom of spirit . She has distinct promise . \" Following her father ’ s death in June 1912 , the seventeen @-@ year @-@ old Barker submitted art and poetry to My Magazine , Child ’ s Own , Leading <unk> , and Raphael Tuck <unk> in an effort to support both her mother and sister . Her sister Dorothy taught kindergarten in two private schools before opening a kindergarten at home . She brought in some money for the family \\'s support while supervising the household . = = = Flower Fairies of the Spring , 1923 = = = Fairies became a popular theme in art and literature in the early 20th century following the releases of The Coming of the Fairies by Sir Arthur Conan Doyle , Peter Pan by <unk> <unk> , and the fairy @-@ themed work of Australian Ida <unk> <unk> . Queen Mary made such themes even more popular by sending <unk> postcards to friends during the 1920s . In 1918 , Barker produced a <unk> series depicting elves and fairies . In 1923 , Barker sent her flower fairy paintings to various publishers . Blackie paid £ 25 for 24 paintings with accompanying verses , but it wasn \\'t until publication of Flower Fairies of the Summer in 1925 that Barker received royalties for her work . Mary <unk> Clayton Calthrop , wife of author Dion Clayton Calthrop , wrote in April 1925 about Barker and Flower Fairies of the Spring : \" She has such exquisite taste , besides <unk> . \" = = = The Waldrons = = = In 1924 , the family moved into a four @-@ level , semi @-@ detached Victorian house at 23 The Waldrons . Barker had a studio built in the garden and her sister conducted a kindergarten in a room at the back of the house . The family lived <unk> and attended both St. Edmund \\'s and St. Andrew \\'s in Croydon – \" low \" churches for the less privileged . Barker sometimes incorporated portraits of her fellow parishioners in her religious works . She was described by Canon Ingram Hill as \" one of the pillars \" of St. Andrew \\'s . The children in the kindergarten modelled for the Flower Fairies until the kindergarten closed in 1940 . In an interview in 1958 , Barker said , \" My sister ran a kindergarten and I used to borrow her students for models . For many years I had an atmosphere of children about me – I never <unk> it . \" She also painted the children of relatives as well as Gladys <unk> , the <unk> \\' young housekeeper , who posed for the Primrose Fairy in 1923 . The plants were painted from life , and if a specimen was not readily at hand , <unk> Gardens staff would provide her the specimens needed . Barker designed and built the Flower Fairy costumes , and based each on the flowers and leaves of the particular plant to be illustrated . The costumes were kept in a trunk in her studio along with wings made of twigs and <unk> . Each was broken down after an illustration was completed and the parts recycled for other costumes . She often referred to Dion Clayton Calthrop \\'s English Costume . = = = Middle years = = = In the late 1920s , Barker began to doubt she was doing enough for the church and considered focusing solely on sacred works . Family and friends recommended she continue secular and sacred works , which she did . Barker continued to attend evening classes at the Croydon Art School between the 1920s and the 1940s , eventually receiving a teaching position . She took <unk> trips to <unk> and Storrington in Sussex and to Cornwall and the southern coast with family and friends . She visited and stayed with artist Margaret <unk> in <unk> , Surrey and with family in <unk> , Near Whitby , North Yorkshire . In 1940 , the Barker \\'s live @-@ in maid retired , and Dorothy Barker closed her school at the back of the house in The Waldrons . She continued to supervise the household , and to give both her mother and sister the care they needed . Dorothy and her sister collaborated upon only two books : Our Darling \\'s First Book and the Christian @-@ themed , He Leadeth Me . In 1954 Dorothy Barker died of a heart attack . Barker was unable to pursue her art to any significant extent following her sister \\'s death , as all the care of her aged mother devolved upon her , but she did manage to begin planning a stained glass window design in her sister \\'s memory for St. Edmund \\'s , Pitlake . = = = Later life and death = = = Barker \\'s mother died in 1960 , and , in 1961 , Barker moved from 23 The Waldrons to 6 <unk> Avenue in Croydon . She restored a <unk> in Storrington , Sussex , England , bequeathed by her friend Edith Major , and named it St. Andrew \\'s . After taking up residence , her health began to deteriorate . She was in and out of nursing and <unk> homes , and tended by relatives and friends . Barker died at Worthing Hospital on 16 February 1973 , aged 77 years . Two funeral services were held – one in Storrington Church and one in Barker \\'s <unk> . Her ashes were scattered in Storrington churchyard . In 1989 , Frederick Warne , a division of Penguin Books since 1983 , acquired the Flower Fairies properties . = = Art = = Barker worked principally in <unk> with pen @-@ and @-@ ink , but she was equally competent in black @-@ and @-@ white , in oils , and in <unk> . She carried a <unk> with her for capturing interesting children . She once indicated , \" I have always tried to paint instinctively in a way that comes naturally to me , without any real thought or attention to artistic theories . \" Kate Greenaway was a childhood favorite and an influence on her art . Barker \\'s child subjects wear nostalgic clothing as Greenaway \\'s children do , though Barker \\'s children are less melancholy and less flat in appearance , due perhaps to advances in printing technology . Barker studied flowers with an analytical eye and was friend to children \\'s illustrator , Margaret <unk> . Along with Greenaway , illustrator Alice B. Woodward also influenced Barker \\'s work . The Pre @-@ <unk> were a strong , lifelong influence on Barker . She once indicated , \" I am to some extent influenced by them — not in any technical sense , but in the choice of subject matter and the feeling and atmosphere they could achieve . \" She admitted a fondness for the early paintings of John Everett Millais and \" the wonderful things \" of Edward <unk> @-@ Jones . = = = Depictions of children = = = Barker \\'s sketches , drawings , and paintings of children were given to friends or to the parents of the subjects , donated to charitable institutions and church sponsored events , or exhibited through various art organizations . She illustrated magazine covers , dust jackets , and produced series of postcards for Raphael Tuck and other publishers such as <unk> Children of the Allies ( 1915 ) , Seaside <unk> ( 1918 ) , and Shakespeare \\'s Boy and Girl Characters ( 1917 , 1920 ) . Her own Old Rhymes for All Times ( 1928 ) and The Lord of the Rushie River ( 1938 ) , a tale about a girl who lives among <unk> on a <unk> , were critically well received . Set about 1800 , Groundsel and Necklaces ( 1943 ) tells of a girl named Jenny who rescues her family from poverty through the agency of the fairies . The story features an old <unk> @-@ like man called Mr. <unk> and <unk> suggests a <unk> social consciousness . Simon the Swan , intended as a sequel to Rushie River was outlined in 1943 with Groundsel , but only developed in 1953 . It was published posthumously in 1988 and is critically considered less successful than Groundsel . = = = Christian @-@ themed works = = = Barker was a devout Christian , and produced religious @-@ themed works throughout her life . She published eight postcards and five guardian angel birthday cards for the Society for <unk> Christian Knowledge in 1916 and in 1923 respectively . Christmas cards were designed for The Girls \\' Friendly Society over a 20 @-@ year period , and the first three designs sold out a combined printing of 46 @,@ 500 in 1923 . An original design for the society called The Darling of the World Has Come was purchased by Queen Mary for ₤ 5 @.@ 5 @.@ 0 in 1926 . The Croydon Art Society hung Barker \\'s booklet cover design for the Society for the <unk> of the Gospel in its November 1919 exhibition . Religious @-@ themed books include The Children \\'s Book of Hymns ( 1929 ) and He Leadeth Me ( 1933 ) , the latter written in collaboration with her sister . Major religious works include the <unk> in oil , The Feeding of the Five Thousand ( 1929 ) , for the chapel in Llandaff House , a home for destitute women at Penarth , Wales , and The Parable of the Great Supper ( 1934 ) for St. George \\'s Chapel , Waddon . The Feeding has since disappeared , and only a black @-@ and @-@ white photograph dated 1929 <unk> the work . In 1941 , she completed oil panels on the subject of the seven sacraments for the baptismal font at St. Andrew \\'s , South Croydon . She designed baptismal rolls for the wall behind the font in 1948 and 1962 . In 1946 , she completed the 4 x 7 <unk> oil painting , Out of Great <unk> , for the Memorial Chapel of <unk> Methodist Church . Following the death of her sister in 1954 , Barker began designs for a stained glass memorial window depicting Christ preparing to wash the feet of his disciples . Her last religious @-@ themed work , it was installed in St. Edmund \\'s , Pitlake , in 1962 . = = Works = = = = = Cards = = = <unk> Children of the Allies ; J. Salmon , 1916 National Mission ; Society for the Preservation of Christian Knowledge , 1916 Shakespeare \\'s Boy Characters ; C. W. Faulkner , 1917 Shakespeare \\'s Girl Characters ; C. W. Faulkner , 1920 Seaside Holiday ; J. Salmon , 1918 , 1921 <unk> and Fairies ; S. Harvey , 1918 Guardian Angel ; Society for the Preservation of Christian Knowledge , 1923 Christmas cards ; Girls \\' Friendly Society , 1920s , 1930s Christmas cards ( US ) ; Barton @-@ Colton , 1920s , 1930s Beautiful Bible Pictures ; Blackie , 1932 = = = Books = = = Flower Fairies of the Spring ; Blackie , 1923 Spring Songs with Music ; Blackie , 1923 Flower Fairies of the Summer ; Blackie , 1925 Child <unk> in Picture and Verse ( by M. K. <unk> ) ; Blackie , 1925 Flower Fairies of the Autumn ; Blackie , 1926 Summer Songs with Music ; Blackie , 1926 The Book of the Flower Fairies ; Blackie , 1927 Autumn Songs with Music ; Blackie , 1927 Old Rhymes for All Times ; Blackie , 1928 The Children ’ s Book of Hymns ; Blackie , 1929 ; <unk> . 1933 Our Darling ’ s First Book ( written in collaboration with Dorothy Barker ) ; Blackie , 1929 The Little Picture Hymn Book ; Blackie , 1933 Rhymes New and Old ; Blackie , 1933 A Flower Fairy Alphabet ; Blackie , 1934 A Little Book of Old Rhymes ; Blackie , 1936 He Leadeth Me ( written in collaboration with Dorothy Barker ) ; Blackie , 1936 A Little Book of Rhymes New and Old ; Blackie , 1937 The Lord of the Rushie River ; Blackie , 1938 Flower Fairies of the Trees ; Blackie , 1940 When Spring <unk> In at the Window ; Blackie , 1942 A Child ’ s Garden of <unk> ( Robert Louis Stevenson ) ; Blackie , 1944 Flower Fairies of the Garden ; Blackie , 1944 Groundsel and Necklaces ; Blackie , 1946 ; reprinted as Fairy Necklaces Flower Fairies of the <unk> ; Blackie , 1948 Flower Fairies of the Flowers and Trees ; Blackie , 1950 Lively Stories ; Macmillan , 1954 The Flower Fairy Picture Book ; Blackie , 1955 Lively Numbers ; Macmillan , 1957 Lively Words ; Macmillan , 1961 . The Sand , the Sea and the Sun ; Gibson , 1970 = = = = <unk> published = = = = Flower Fairies of the Winter ; Blackie , 1985 Simon the Swan ; Blackie , 1988 Flower Fairies of the Seasons ; <unk> / Blackie , 1988 A Little Book of <unk> and Hymns ; Frederick Warne , 1994 A Flower Fairies Treasury ; Frederick Warne , 1997 <unk> ; Frederick Warne , 2005 Wild Cherry Makes A Wish ; ( collaboration with <unk> Le <unk> ) Frederick Warne , 2006 How to find Flower Fairies ; Frederick Warne , 2007 Return to <unk> ; Frederick Warne , 2008 = = = Book covers = = = A New Epiphany ; Society for the Preservation of Christian Knowledge , 1919 43 <unk> ; Blackie , 1920s , 1930s = = = Religious works = = = St. Cecily \\'s Garden ; 1920 <unk> roll design ; St. Edmund \\'s , Pitlake , 1922 Banner design ; St. Mary \\'s , <unk> , 1923 The Feeding of the Five Thousand ; <unk> <unk> , chapel at Penarth , Wales ; 1929 The Parable of the Great Supper ; <unk> , St. George \\'s chapel , Waddon The Seven <unk> ; baptismal font panels , St. Andrew \\'s , Croydon St. John the Baptist ; central banner panel , <unk> church , 1943 <unk> , sword , and shield ; mount for a list of men and woman serving in the Forces , St. Andrews , Croydon , 1943 <unk> rolls ; St. Andrews , Croydon , 1948 , 1962 The font in St Andrew \\'s Church , South Croydon Out of Great <unk> ; memorial chapel , <unk> <unk> church , 1948 I Am Among You As He That <unk> ; stained glass window design , St. Edmund \\'s , Pitlake , 1962'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DY2tWwzZwDDV"
      },
      "source": [
        "Initialize new model with default parameters (data isn't preprocessed)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IaxNn5HHLiM"
      },
      "source": [
        "topic_model_default = BERTopic(language=\"english\", calculate_probabilities=True, verbose=True)"
      ],
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1rU_m7UwneX"
      },
      "source": [
        "Fit model with train dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122,
          "referenced_widgets": [
            "dc201bed97ad4357abb0265dafec3e1b",
            "bb42f0d77d734a3981be93c0eea32ec7",
            "a1a0368422854be3a64cf4911a622d78",
            "7d90d7c5b71046a4ad08001b150bcede",
            "bb024d6eac2b47dc9388443ce9859d5f",
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        "id": "4R3Sjx55wjKB",
        "outputId": "a90910f0-3847-4248-aee1-c205ee49c5f8"
      },
      "source": [
        "topics_default, probs_default = topic_model_default.fit_transform(docs)"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc201bed97ad4357abb0265dafec3e1b",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Batches', max=24.0, style=ProgressStyle(description_width…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2021-07-28 07:23:33,555 - BERTopic - Transformed documents to Embeddings\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-07-28 07:23:38,808 - BERTopic - Reduced dimensionality with UMAP\n",
            "2021-07-28 07:23:38,868 - BERTopic - Clustered UMAP embeddings with HDBSCAN\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u50TtuO8yaFH"
      },
      "source": [
        "Quantity of documents are assigned to a topic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "ycLrRATEymJk",
        "outputId": "83ae384d-75bb-4648-8481-09e1d615ff1d"
      },
      "source": [
        "topic_model_default.get_topic_info()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>178</td>\n",
              "      <td>-1_was_as_his_with</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>0_he_his_season_first</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>1_song_album_music_songs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>55</td>\n",
              "      <td>2_episode_her_she_he</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>49</td>\n",
              "      <td>3_were_ships_ship_guns</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>43</td>\n",
              "      <td>4_route_highway_road_ny</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>42</td>\n",
              "      <td>5_species_with_have_has</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>33</td>\n",
              "      <td>6_his_he_king_john</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>7_storm_hurricane_winds_cyclone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8</td>\n",
              "      <td>25</td>\n",
              "      <td>8_australian_air_aircraft_war</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>9_game_guitar_hero_games</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>10_church_cathedral_scientology_was</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "      <td>11_are_proteins_protein_cell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>12_ireland_unk_haifa_city</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13_nr_wins_losses_ties</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>14_stars_star_cadmium_jupiter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "      <td>15_her_she_eva_fey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>16_river_dam_missouri_galveston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>17_his_wheeler_busch_in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18</td>\n",
              "      <td>10</td>\n",
              "      <td>18_film_hibari_sholay_her</td>\n",
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              "    Topic  Count                                 Name\n",
              "0      -1    178                   -1_was_as_his_with\n",
              "1       0     76                0_he_his_season_first\n",
              "2       1     73             1_song_album_music_songs\n",
              "3       2     55                 2_episode_her_she_he\n",
              "4       3     49               3_were_ships_ship_guns\n",
              "5       4     43              4_route_highway_road_ny\n",
              "6       5     42              5_species_with_have_has\n",
              "7       6     33                   6_his_he_king_john\n",
              "8       7     31      7_storm_hurricane_winds_cyclone\n",
              "10      8     25        8_australian_air_aircraft_war\n",
              "9       9     25             9_game_guitar_hero_games\n",
              "11     10     19  10_church_cathedral_scientology_was\n",
              "12     11     18         11_are_proteins_protein_cell\n",
              "13     12     17            12_ireland_unk_haifa_city\n",
              "14     13     13               13_nr_wins_losses_ties\n",
              "15     14     13        14_stars_star_cadmium_jupiter\n",
              "16     15     12                   15_her_she_eva_fey\n",
              "17     16     11      16_river_dam_missouri_galveston\n",
              "18     17     10              17_his_wheeler_busch_in\n",
              "19     18     10            18_film_hibari_sholay_her"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pH1f9T-0zMZx"
      },
      "source": [
        "*We examine specific topic, in this case is topic 0*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5xtt3gYy9F2",
        "outputId": "a2bea12d-f6dd-4a0f-e7e5-5523fc8818a3"
      },
      "source": [
        "topic_model_default.get_topic(2)"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('album', 0.05475474984094957),\n",
              " ('song', 0.05247642175644389),\n",
              " ('songs', 0.024933615515251344),\n",
              " ('dylan', 0.024013155143533627),\n",
              " ('carey', 0.018255159079420036),\n",
              " ('recording', 0.015016510724405492),\n",
              " ('recorded', 0.013111084734772216),\n",
              " ('lyrics', 0.012383771178515367),\n",
              " ('vocals', 0.011050798453406452),\n",
              " ('albums', 0.011016638009398463)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MWY00vxYzPSW"
      },
      "source": [
        "**This topic has high probability to be allocated to MUSIC topic**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "_lYM5sDmKac4",
        "outputId": "aac1cc5a-2452-4c8a-8f62-c5aed15c5b86"
      },
      "source": [
        " topic_model_default.visualize_topics()"
      ],
      "execution_count": 144,
      "outputs": [
        {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txqm5FoX0BSj"
      },
      "source": [
        "**In this scenario, we didn't remove Stopwords (he, she, the, is,...) and Unknown words (<unk>) and it's affecting to our results.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QB2jyWZN0XKk"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78VgsdyU21Df"
      },
      "source": [
        "###**3.2. Remove Stopword and Unknown**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21g9V3tL8uG3"
      },
      "source": [
        "Below function filters each of documents to remove stopword and unknown"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrl-GA0x69r7"
      },
      "source": [
        "def remove_words(documents):\n",
        "  new_documents = []\n",
        "  for doc in documents:\n",
        "    new_doc = remove_stopwords(doc)          \n",
        "    new_doc = new_doc.replace(\"<unk>\", \"\")  \n",
        "\n",
        "    new_documents.append(new_doc)\n",
        "\n",
        "  return new_documents"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJN4Ku1b9ahm"
      },
      "source": [
        "data_train = read_wiki(\"wiki.train.tokens\")\n",
        "docs = list(data_train)"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4s5jubm9dvc"
      },
      "source": [
        "docs = remove_words(docs)"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXl9sddM9Wrd"
      },
      "source": [
        "Repeat steps above to retrain model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OzqQdss85U9"
      },
      "source": [
        "topic_model_word_removed = BERTopic(language=\"english\", calculate_probabilities=True, verbose=True)"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "referenced_widgets": [
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        "outputId": "91cfb9b6-e13b-4558-d94c-240677a176a0"
      },
      "source": [
        "topics_word_removed, probs_word_removed = topic_model_word_removed.fit_transform(docs)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96ad9d01ae8a48fdb0480bf55dc8b608",
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              "HBox(children=(FloatProgress(value=0.0, description='Batches', max=24.0, style=ProgressStyle(description_width…"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "2021-07-28 07:15:45,646 - BERTopic - Transformed documents to Embeddings\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-07-28 07:15:50,890 - BERTopic - Reduced dimensionality with UMAP\n",
            "2021-07-28 07:15:50,953 - BERTopic - Clustered UMAP embeddings with HDBSCAN\n"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n75qeWED91pY"
      },
      "source": [
        "Information of topics after removing words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "xeGeDAM79zbB",
        "outputId": "b0755628-b4a0-436d-994f-659f6c4184d3"
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      "source": [
        "topic_model_word_removed.get_topic_info()"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
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              "      <td>5_species_birds_genus_kakapo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>38</td>\n",
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              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>7_poem_book_poetry_poems</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>33</td>\n",
              "      <td>8_storm_hurricane_winds_cyclone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>31</td>\n",
              "      <td>9_john_rosebery_churchill_king</td>\n",
              "    </tr>\n",
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              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>31</td>\n",
              "      <td>10_game_hero_games_released</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
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              "      <td>30</td>\n",
              "      <td>11_stars_star_cadmium_nuclear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>13_track_athletes_competitions_athletics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>17</td>\n",
              "      <td>14_australian_australia_aircraft_squadron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>15_film_ali_telugu_released</td>\n",
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              "    Topic  Count                                       Name\n",
              "0      -1    127          -1_scientology_century_war_church\n",
              "1       0     78                0_game_won_championship_win\n",
              "2       1     76                   1_album_song_songs_dylan\n",
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              "4       3     55              3_ships_ship_brigade_infantry\n",
              "5       4     51          4_episode_series_rachel_character\n",
              "6       5     43               5_species_birds_genus_kakapo\n",
              "7       6     38             6_church_haifa_cathedral_built\n",
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              "9       8     33            8_storm_hurricane_winds_cyclone\n",
              "10      9     31             9_john_rosebery_churchill_king\n",
              "11     10     31                10_game_hero_games_released\n",
              "12     11     30              11_stars_star_cadmium_nuclear\n",
              "13     12     21             12_film_wilde_anderson_theatre\n",
              "14     13     18   13_track_athletes_competitions_athletics\n",
              "15     14     17  14_australian_australia_aircraft_squadron\n",
              "16     15     13                15_film_ali_telugu_released"
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        }
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    },
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "Visualize result of topics after removing words"
      ]
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    {
      "cell_type": "code",
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      "outputs": [
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          "metadata": {
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    {
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      "metadata": {
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      "source": [
        "topic_model_word_removed.get_topic(7)"
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      "execution_count": 180,
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          "data": {
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              "[('poem', 0.02895436100562492),\n",
              " ('book', 0.018500695773743425),\n",
              " ('poetry', 0.01472020873773619),\n",
              " ('poems', 0.013517322674881864),\n",
              " ('keats', 0.012975981985054199),\n",
              " ('tennyson', 0.01148870810750142),\n",
              " ('ulysses', 0.010148164663459767),\n",
              " ('wrote', 0.01002148070860962),\n",
              " ('poet', 0.009764157199366906),\n",
              " ('nightingale', 0.009246899630430634)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
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      "source": [
        "topic_model_word_removed.visualize_barchart(top_n_topics=10)"
      ],
      "execution_count": 191,
      "outputs": [
        {
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkkWCD4b-0gB"
      },
      "source": [
        "**After removing stopword and unknown but still remaining order of words in sequences, the result is better**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whN1lEK388Cc"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcieTEmW0eVz"
      },
      "source": [
        "### 3.2. Multilingual"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9DwAeD90ijG"
      },
      "source": [
        "We recognize the dataset has documents in other languages (Japaneses, ...)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "-SuysCNeNdVH",
        "outputId": "60005d52-2b34-4305-d8be-02509c69fde7"
      },
      "source": [
        "data_train = read_wiki(\"wiki.train.tokens\")\n",
        "docs = list(data_train)\n",
        "docs[0]"
      ],
      "execution_count": 192,
      "outputs": [
        {
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              "'= Valkyria Chronicles III = Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to as Valkyria Chronicles III outside Japan , is a tactical role @-@ playing video game developed by Sega and Media.Vision for the PlayStation Portable . Released in January 2011 in Japan , it is the third game in the Valkyria series . <unk> the same fusion of tactical and real @-@ time gameplay as its predecessors , the story runs parallel to the first game and follows the \" Nameless \" , a penal military unit serving the nation of Gallia during the Second Europan War who perform secret black operations and are pitted against the Imperial unit \" <unk> Raven \" . The game began development in 2010 , carrying over a large portion of the work done on Valkyria Chronicles II . While it retained the standard features of the series , it also underwent multiple adjustments , such as making the game more <unk> for series newcomers . Character designer <unk> Honjou and composer Hitoshi Sakimoto both returned from previous entries , along with Valkyria Chronicles II director Takeshi Ozawa . A large team of writers handled the script . The game \\'s opening theme was sung by May \\'n . It met with positive sales in Japan , and was praised by both Japanese and western critics . After release , it received downloadable content , along with an expanded edition in November of that year . It was also adapted into manga and an original video animation series . Due to low sales of Valkyria Chronicles II , Valkyria Chronicles III was not localized , but a fan translation compatible with the game \\'s expanded edition was released in 2014 . Media.Vision would return to the franchise with the development of Valkyria : Azure Revolution for the PlayStation 4 . = = Gameplay = = As with previous <unk> Chronicles games , Valkyria Chronicles III is a tactical role @-@ playing game where players take control of a military unit and take part in missions against enemy forces . Stories are told through comic book @-@ like panels with animated character portraits , with characters speaking partially through voiced speech bubbles and partially through <unk> text . The player progresses through a series of linear missions , gradually unlocked as maps that can be freely <unk> through and replayed as they are unlocked . The route to each story location on the map varies depending on an individual player \\'s approach : when one option is selected , the other is sealed off to the player . Outside missions , the player characters rest in a camp , where units can be customized and character growth occurs . Alongside the main story missions are character @-@ specific sub missions relating to different squad members . After the game \\'s completion , additional episodes are unlocked , some of them having a higher difficulty than those found in the rest of the game . There are also love simulation elements related to the game \\'s two main <unk> , although they take a very minor role . The game \\'s battle system , the <unk> system , is carried over directly from <unk> Chronicles . During missions , players select each unit using a top @-@ down perspective of the battlefield map : once a character is selected , the player moves the character around the battlefield in third @-@ person . A character can only act once per @-@ turn , but characters can be granted multiple turns at the expense of other characters \\' turns . Each character has a field and distance of movement limited by their Action <unk> . Up to nine characters can be assigned to a single mission . During gameplay , characters will call out if something happens to them , such as their health points ( HP ) getting low or being knocked out by enemy attacks . Each character has specific \" Potentials \" , skills unique to each character . They are divided into \" Personal Potential \" , which are innate skills that remain unaltered unless otherwise dictated by the story and can either help or impede a character , and \" Battle Potentials \" , which are grown throughout the game and always grant <unk> to a character . To learn Battle Potentials , each character has a unique \" Masters Table \" , a grid @-@ based skill table that can be used to acquire and link different skills . Characters also have Special <unk> that grant them temporary <unk> on the battlefield : Kurt can activate \" Direct Command \" and move around the battlefield without <unk> his Action Point gauge , the character <unk> can shift into her \" Valkyria Form \" and become <unk> , while Imca can target multiple enemy units with her heavy weapon . Troops are divided into five classes : Scouts , <unk> , Engineers , <unk> and Armored Soldier . <unk> can switch classes by changing their assigned weapon . Changing class does not greatly affect the stats gained while in a previous class . With victory in battle , experience points are awarded to the squad , which are distributed into five different attributes shared by the entire squad , a feature differing from early games \\' method of distributing to different unit types . = = Plot = = The game takes place during the Second Europan War . Gallian Army Squad 422 , also known as \" The Nameless \" , are a penal military unit composed of criminals , foreign <unk> , and military offenders whose real names are erased from the records and <unk> officially referred to by numbers . <unk> by the Gallian military to perform the most dangerous missions that the Regular Army and Militia will not do , they are nevertheless up to the task , exemplified by their motto , <unk> <unk> , meaning \" Always Ready . \" The three main characters are <unk> Kurt Irving , an army officer falsely accused of treason who wishes to redeem himself ; Ace <unk> Imca , a female Darcsen heavy weapons specialist who seeks revenge against the Valkyria who destroyed her home ; and <unk> Riela <unk> , a seemingly <unk> young woman who is unknowingly a descendant of the Valkyria . Together with their fellow squad members , these three are tasked to fight against a mysterious Imperial unit known as Calamity Raven , consisting of mostly Darcsen soldiers . As the Nameless officially do not exist , the upper echelons of the Gallian Army exploit the concept of plausible <unk> in order to send them on missions that would otherwise make Gallia lose face in the war . While at times this works to their advantage , such as a successful incursion into Imperial territory , other orders cause certain members of the 422nd great distress . One such member , <unk> , becomes so enraged that he abandons his post and defects into the ranks of Calamity Raven , attached to the ideal of Darcsen independence proposed by their leader , Dahau . At the same time , elements within Gallian Army Command move to erase the Nameless in order to protect their own interests . <unk> by both allies and enemies , and combined with the presence of a traitor within their ranks , the 422nd desperately move to keep themselves alive while at the same time fight to help the Gallian war effort . This continues until the Nameless \\'s commanding officer , Ramsey Crowe , who had been kept under house arrest , is escorted to the capital city of <unk> in order to present evidence <unk> the weary soldiers and expose the real traitor , the Gallian General that had accused Kurt of Treason . <unk> due to these events , and partly due to the major losses in manpower Gallia suffers towards the end of the war with the Empire , the Nameless are offered a formal position as a squad in the Gallian Army rather than serve as an anonymous shadow force . This is short @-@ lived , however , as following Maximilian \\'s defeat , Dahau and Calamity Raven move to activate an ancient <unk> super weapon within the Empire , kept secret by their benefactor . Without the support of Maximilian or the chance to prove themselves in the war with Gallia , it is Dahau \\'s last <unk> card in creating a new Darcsen nation . As an armed Gallian force invading the Empire just following the two nations \\' cease @-@ fire would certainly wreck their newfound peace , Kurt decides to once again make his squad the Nameless , asking Crowe to list himself and all under his command as killed @-@ in @-@ action . Now owing allegiance to none other than themselves , the 422nd confronts Dahau and destroys the <unk> weapon . Each member then goes their separate ways in order to begin their lives <unk> . = = Development = = Concept work for Valkyria Chronicles III began after development finished on Valkyria Chronicles II in early 2010 , with full development beginning shortly after this . The director of Valkyria Chronicles II , Takeshi Ozawa , returned to that role for Valkyria Chronicles III . Development work took approximately one year . After the release of Valkyria Chronicles II , the staff took a look at both the popular response for the game and what they wanted to do next for the series . Like its predecessor , Valkyria Chronicles III was developed for PlayStation Portable : this was due to the team wanting to refine the mechanics created for Valkyria Chronicles II , and they had not come up with the \" revolutionary \" idea that would warrant a new entry for the PlayStation 3 . Speaking in an interview , it was stated that the development team considered Valkyria Chronicles III to be the series \\' first true sequel : while Valkyria Chronicles II had required a large amount of trial and error during development due to the platform move , the third game gave them a chance to improve upon the best parts of Valkyria Chronicles II due to being on the same platform . In addition to Sega staff from the previous games , development work was also handled by <unk> The original scenario was written <unk> <unk> , while the script was written by Hiroyuki <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> and <unk> <unk> . Its story was darker and more somber than that of its predecessor . The majority of material created for previous games , such as the <unk> system and the design of maps , was carried over . Alongside this , improvements were made to the game \\'s graphics and some elements were expanded , such as map layouts , mission structure , and the number of playable units per mission . A part of this upgrade involved creating unique <unk> models for each character \\'s body . In order to achieve this , the cooperative elements incorporated into the second game were removed , as they took up a large portion of memory space needed for the improvements . They also adjusted the difficulty settings and ease of play so they could appeal to new players while retaining the essential components of the series \\' gameplay . The newer systems were decided upon early in development . The character designs were done by <unk> Honjou , who had worked on the previous Valkyria Chronicles games . When creating the Nameless Squad , Honjou was faced with the same problem he had had during the first game : the military uniforms essentially destroyed character individuality , despite him needing to create unique characters the player could identify while maintaining a sense of reality within the Valkyria Chronicles world . The main color of the Nameless was black . As with the previous Valkyria games , Valkyria Chronicles III used the <unk> graphics engine . The anime opening was produced by Production I.G. = = = Music = = = The music was composed by Hitoshi Sakimoto , who had also worked on the previous Valkyria Chronicles games . When he originally heard about the project , he thought it would be a light tone similar to other Valkyria Chronicles games , but found the themes much darker than expected . An early theme he designed around his original vision of the project was rejected . He <unk> the main theme about seven times through the music production due to this need to <unk> the game . The main theme was initially recorded using orchestra , then Sakimoto removed elements such as the guitar and bass , then adjusted the theme using a synthesizer before <unk> segments such as the guitar piece on their own before incorporating them into the theme . The rejected main theme was used as a hopeful tune that played during the game \\'s ending . The battle themes were designed around the concept of a \" modern battle \" divorced from a fantasy scenario by using modern musical instruments , constructed to create a sense of <unk> . While Sakimoto was most used to working with synthesized music , he felt that he needed to incorporate live instruments such as orchestra and guitar . The guitar was played by <unk> <unk> , who also arranged several of the later tracks . The game \\'s opening theme song , \" If You Wish for ... \" ( <unk> , <unk> Kimi <unk> <unk> <unk> ) , was sung by Japanese singer May \\'n . Its theme was the reason soldiers fought , in particular their wish to protect what was precious to them rather than a sense of responsibility or duty . Its lyrics were written by <unk> <unk> , who had worked on May \\'n on previous singles . = = = Release = = = In September 2010 , a teaser website was revealed by Sega , hinting at a new Valkyria Chronicles game . In its September issue , Famitsu listed that Senjō no Valkyria 3 would be arriving on the PlayStation Portable . Its first public appearance was at the 2010 Tokyo Game Show ( TGS ) , where a demo was made available for journalists and attendees . During the publicity , story details were kept <unk> so as not to <unk> too much for potential players , along with some of its content still being in flux at the time of its reveal . To promote the game and detail the story leading into the game \\'s events , an episodic Flash visual novel written by <unk> began release in January 2011 . The game was released January 27 , 2011 . During an interview , the development team said that the game had the capacity for downloadable content ( DLC ) , but that no plans were finalized . Multiple DLC maps , featuring additional missions and <unk> characters , were released between February and April 2011 . An expanded edition of the game , Valkyria Chronicles III Extra Edition , released on November 23 , 2011 . <unk> and sold at a lower price than the original , Extra Edition game with seven additional episodes : three new , three chosen by staff from the game \\'s DLC , and one made available as a pre @-@ order bonus . People who also owned the original game could transfer their save data between versions . Unlike its two predecessors , Valkyria Chronicles III was not released in the west . According to Sega , this was due to poor sales of Valkyria Chronicles II and the general unpopularity of the PSP in the west . An unofficial fan translation patch began development in February 2012 : players with a copy of Valkyria Chronicles III could download and apply the patch , which translated the game \\'s text into English . <unk> with the Extra Edition , the patch was released in January 2014 . = = Reception = = On its day of release in Japan , Valkyria Chronicles III topped both platform @-@ exclusive and multi @-@ platform sales charts . By early February , the game sold 102 @,@ <unk> units , coming in second overall to The Last Story for the Wii . By the end of the year , the game had sold just over 152 @,@ 500 units . Famitsu enjoyed the story , and were particularly pleased with the improvements to gameplay . Japanese gaming site Game Watch <unk> , despite negatively noting its pacing and elements recycled from previous games , was generally positive about its story and characters , and found its gameplay entertaining despite off @-@ putting difficulty spikes . <unk> writer <unk> <unk> , in a \" Play Test \" article based on the game \\'s <unk> demo , felt that Valkyria Chronicles III provided a \" profound feeling of closure \" for the Valkyria Chronicles series . He praised its gameplay despite annoying limitations to aspects such as special abilities , and positively noted its shift in story to a tone similar to the first game . PlayStation Official Magazine - UK praised the story \\'s <unk> of Gallia \\'s moral standing , art style , and most points about its gameplay , positively noting the latter for both its continued quality and the tweaks to balance and content . Its one major criticism were multiple difficulty spikes , something that had affected the previous games . Heath Hindman of gaming website PlayStation <unk> praised the addition of non @-@ linear elements and improvements or removal of mechanics from Valkyria Chronicles II in addition to praising the returning gameplay style of previous games . He also positively noted the story \\'s serious tone . Points criticized in the review were recycled elements , awkward cutscenes that seemed to include all characters in a scene for no good reason , pacing issues , and occasional problems with the game \\'s AI . In a preview of the TGS demo , Ryan Geddes of IGN was left excited as to where the game would go after completing the demo , along with enjoying the improved visuals over Valkyria Chronicles II . Kotaku \\'s Richard <unk> was highly positive about the game , citing is story as a return to form after Valkyria Chronicles II and its gameplay being the best in the series . His main criticisms were its length and gameplay repetition , along with expressing regret that it would not be localized . = = Legacy = = Kurt and Riela were featured in the Nintendo 3DS crossover Project X Zone , representing the Valkyria series . Media.Vision would return to the series to develop Valkyria : Azure Revolution , with Ozawa returning as director . Azure Revolution is a role @-@ playing video game for the PlayStation 4 that forms the beginning of a new series within the Valkyria franchise . = = = Adaptations = = = Valkyria Chronicles 3 was adapted into a two @-@ episode original video animation series in the same year of its release . <unk> Senjō no Valkyria 3 : <unk> <unk> no <unk> ( <unk> <unk> , lit . Valkyria of the Battlefield 3 : The <unk> Taken for <unk> \\'s Sake ) , it was originally released through PlayStation Network and <unk> between April and May 2011 . The initially @-@ planned release and availability period needed to be extended due to a stoppage to <unk> during the early summer of that year . It later released for DVD on June 29 and August 31 , 2011 , with separate \" Black \" and \" Blue \" editions being available for purchase . The anime is set during the latter half of Valkyria Chronicles III , detailing a mission by the Nameless against their Imperial rivals Calamity Raven . The anime was first announced in November 2010 . It was developed by A @-@ 1 Pictures , produced by Shinji <unk> , directed by <unk> <unk> , and written by Hiroshi <unk> . Sakimoto \\'s music for the game was used in the anime . The anime \\'s title was inspired by the principle purpose of the Nameless : to suffer in battle for the goals of others . A <unk> attached to the project during development was \" The Road to <unk> \" , which referenced the <unk> Tank Museum in Moscow . The game \\'s main theme was how the characters regained their sense of self when stripped of their names and identities , along with general themes focused on war and its consequences . While making the anime , the production team were told by Sega to make it as realistic as possible , with the consequence that the team did extensive research into aspects such as what happened when vehicles like tanks were overturned or damaged . Due to it being along the same timeline as the original game and its television anime adaptation , the cast of Valkyria Chronicles could make appearances , which pleased the team . The opening theme , \" <unk> ( Light ) <unk> \" ( <unk> @-@ <unk> ) , was sung by Japanese singer <unk> . The ending theme , \" <unk> the Flowers of Light Will Bloom \" ( <unk> , <unk> <unk> <unk> no <unk> ) , was sung by <unk> <unk> . Both songs \\' lyrics were written by their respective artists . Two manga adaptations were produced , following each of the game \\'s main female protagonists Imca and Riela . They were Senjō no Valkyria 3 : <unk> <unk> <unk> no <unk> ( 戦場のヴァルキュリア3 <unk> , lit . Valkyria of the Battlefield 3 : The Flower of the Nameless Oath ) , illustrated by <unk> <unk> and eventually released in two volumes after being serialized in Dengeki <unk> between 2011 and 2012 ; and Senjō no Valkyria 3 : <unk> <unk> no <unk> <unk> ( 戦場のヴァルキュリア3 <unk> , lit . Valkyria of the Battlefield 3 <unk> <unk> of the Crimson Fate ) , illustrated by <unk> <unk> and eventually released in a single volume by Kadokawa Shoten in 2012 .'"
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        "def remove_words(documents):\n",
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        "    new_doc = remove_stopwords(doc)          \n",
        "    new_doc = new_doc.replace(\"<unk>\", \"\")  \n",
        "\n",
        "    new_documents.append(new_doc)\n",
        "\n",
        "  return new_documents"
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      "outputs": []
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      "metadata": {
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      "source": [
        "docs = remove_words(docs)"
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      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eXY5u4j1d0p"
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      "source": [
        "Repeat steps above to retrain model "
      ]
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      "metadata": {
        "id": "-Lnov0Vm06v8"
      },
      "source": [
        "topic_model_multilanguage = BERTopic(language=\"multilingual\", calculate_probabilities=True, verbose=True)"
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      "outputs": []
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        "topics_multilanguage, probs_multilanguage = topic_model_multilanguage.fit_transform(docs)"
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            "2021-07-28 07:30:45,631 - BERTopic - Transformed documents to Embeddings\n"
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            "2021-07-28 07:30:50,826 - BERTopic - Reduced dimensionality with UMAP\n",
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      "execution_count": 198,
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              "4       3     58                 3_route_river_highway_road\n",
              "5       4     58         4_episode_television_season_rachel\n",
              "6       5     47             5_species_birds_genus_proteins\n",
              "7       6     36                   6_game_hero_guitar_games\n",
              "8       7     34                 7_king_nixon_election_died\n",
              "9       8     32            8_storm_hurricane_winds_cyclone\n",
              "10      9     32                    9_ships_ship_fleet_navy\n",
              "11     10     27             10_stars_xenon_cadmium_jupiter\n",
              "12     11     24        11_book_finkelstein_published_novel\n",
              "13     12     22                  12_film_play_theatre_cast\n",
              "14     13     15  13_australian_aircraft_australia_squadron\n",
              "15     14     13     14_brigade_infantry_battalion_regiment\n",
              "16     15     11              15_poem_tennyson_poetry_poems"
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      "execution_count": 199,
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