clustering_w2v.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "clustering_w2v.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPkPYvW7HKn8pZa9grXopuw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/csralvall/online_game_toxicity/blob/main/clustering_w2v.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VH7vZ2eXXVDD"
      },
      "source": [
        "### Import utility functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFh8HD-mWVnw"
      },
      "source": [
        "from IPython.display import clear_output"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eF3gtzESXiz2"
      },
      "source": [
        "### Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBtxeZnzVXjW"
      },
      "source": [
        "# install dependencies\n",
        "!pip install -U pip setuptools wheel pandas sklearn numpy gensim wget\n",
        "clear_output()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHeftvjrXofB"
      },
      "source": [
        "### Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maXhDvByUuFn"
      },
      "source": [
        "# import libraries\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn.manifold\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.cluster import KMeans\n",
        "from collections import Counter\n",
        "from gensim.models import Word2Vec"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0q1b1zYpkE34"
      },
      "source": [
        "### Setup dataframe print options"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkUytsZfkDqR"
      },
      "source": [
        "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DL1sYT0tXx7k"
      },
      "source": [
        "### Mount storage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3mjkCTQWaLo",
        "outputId": "a30ca582-0c2c-4f84-89b7-6a4a295dc637"
      },
      "source": [
        "# mount google drive unit to save computationally expensive results\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOZY40qrX0ZC"
      },
      "source": [
        "### Load subset of whole dataset from storage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVhIh1OfWkF_"
      },
      "source": [
        "# english chats from original dataset with anotations\n",
        "eng_annotated = '/content/drive/MyDrive/nlp/dota2_chat_eng_annotated_2.csv'\n",
        "df_test = pd.read_csv(eng_annotated)[:10000].dropna().reset_index(drop=True)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yu0UcyGI7GHb"
      },
      "source": [
        "### Get bad word list from memory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnXy-9AwhltM"
      },
      "source": [
        "# get downloaded bad word list\n",
        "word_list = \"/content/drive/MyDrive/nlp/bad_words.txt\"\n",
        "# use set for fast queries\n",
        "bad_words = set(line.strip() for line in open(word_list, 'r'))\n",
        "# add new bad words\n",
        "bad_words.update(['noob', 'noobs', 'stfu', 'fukign', 'fuking', 'fukin', 'nooob'])\n",
        "bad_dict = dict.fromkeys(bad_words, 0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iF5zBmzWqOwf"
      },
      "source": [
        "### Word2Vec embeddings:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OsRlXlT7zs7"
      },
      "source": [
        "# function to create embeddings of words in each chat\n",
        "def generate_embedding_w2v(sentences: [[str]]):\n",
        "  w2v_model = Word2Vec(\n",
        "                     min_count=20,\n",
        "                     window=2,\n",
        "                     #size=300,\n",
        "                     sample=6e-5, \n",
        "                     alpha=0.03, \n",
        "                     min_alpha=0.0007, \n",
        "                     negative=20,\n",
        "                     workers=1)\n",
        "\n",
        "  w2v_model.build_vocab(sentences, progress_per=10000)\n",
        "\n",
        "  w2v_model.train(sentences, total_examples=w2v_model.corpus_count, epochs=30, report_delay=1)\n",
        "\n",
        "  return w2v_model"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6q41eHRYLeD"
      },
      "source": [
        "### Unroll chats as list of words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wet2ZMgEGbzz"
      },
      "source": [
        "def flatten(t):\n",
        "    return [item for sublist in t for item in sublist]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGbEkPMkmdoM"
      },
      "source": [
        "# from cleaned english chats get all of them without nan values\n",
        "chats = df_test[['tokens']].dropna().astype(str).values\n",
        "chats = flatten(chats)\n",
        "vocab = list(map(lambda x: x.split(), chats))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XHxBPDL3BM9"
      },
      "source": [
        "### Generate word embeddings from chat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCC4MAJ63sPm"
      },
      "source": [
        "w2v_model = generate_embedding_w2v(vocab)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukuY_qWGmTXF"
      },
      "source": [
        "# save Word2Vec model\n",
        "w2v_model.save('/content/drive/MyDrive/nlp/word2vec_2.model')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sKtCB-e3LZF"
      },
      "source": [
        "### Load word embeddings from disk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjT9yQPF3tfT"
      },
      "source": [
        "# load Word2Vec model\n",
        "w2v_model = Word2Vec.load('/content/drive/MyDrive/nlp/word2vec_2.model')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suZ6ZD-lvay6"
      },
      "source": [
        "### Embeddings utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AisVg8TSvhUj"
      },
      "source": [
        "# generate embedding from chat\n",
        "def chat_embedding(model, chat_words):\n",
        "    chat_embedding = np.ones(100)\n",
        "    for word in chat_words:\n",
        "        if word in model.wv:\n",
        "            chat_embedding *= model.wv[word] \n",
        "    return chat_embedding"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSssAgSoxrOo"
      },
      "source": [
        "### Clustering utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlacJecnxtnZ"
      },
      "source": [
        "def get_bad_vec(lexicon, chat_words):\n",
        "    for word in chat_words:\n",
        "        if word in lexicon:\n",
        "            lexicon[word] += 1\n",
        "\n",
        "    bad_vec = np.fromiter(lexicon.values(), dtype=int)\n",
        "    \n",
        "    return bad_vec"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0km18782I8rI"
      },
      "source": [
        "intensity = df_test[['intensity']].copy()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4u07Ttn0ljUA"
      },
      "source": [
        "### Create w2v vectors for clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeafPfvlFXRy"
      },
      "source": [
        "w2v_serie = np.array([])\n",
        "for idx, chat in enumerate(chats):\n",
        "    lexicon = dict.fromkeys(bad_words, 0)\n",
        "    chat_intensity = intensity.loc[idx]\n",
        "    chat_words = chat.split()\n",
        "    bad_vec = get_bad_vec(lexicon, chat_words)\n",
        "    w2v_embedding = chat_embedding(w2v_model, chat_words)\n",
        "    w2v_bad_int_vec = np.hstack((w2v_embedding, bad_vec, chat_intensity)).ravel()\n",
        "    w2v_serie = np.concatenate((w2v_serie, w2v_bad_int_vec))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vjt8H2Glrwi"
      },
      "source": [
        "### Reshape data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5ob-oAzwGZC"
      },
      "source": [
        "w2v_ncolumns = w2v_model.wv.vectors.shape[1] + len(bad_words) + 1\n",
        "w2v_serie = w2v_serie.astype('float').reshape((-1, w2v_ncolumns))\n",
        "w2v_matrix = w2v_serie[~np.isnan(w2v_serie)].reshape((-1, w2v_ncolumns))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfRO3gt9lxNs"
      },
      "source": [
        "### Matrix reduction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVZBvVQj6EWR"
      },
      "source": [
        "def reduce_matrix(matrix: np.ndarray, *, variance_treshold: float):\n",
        "    print(f'INPUT SHAPE: {matrix.shape}')\n",
        "    # reduce all vectors to [0, 1] space\n",
        "    normalized_matrix = normalize(matrix, axis=1)\n",
        "    # compute variances in each row\n",
        "    matrix_variances = np.var(matrix, axis=0)\n",
        "    # create mask for features with high correlation (low variance)\n",
        "    bool_mask = np.where(matrix_variances < variance_treshold)\n",
        "    # filter features with high correlation (variance under treshold)\n",
        "    raked_matrix = np.delete(normalized_matrix, bool_mask, axis=1)\n",
        "    print(f'OUTPUT SHAPE: {raked_matrix.shape}')\n",
        "    return raked_matrix"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvqNAUdfhnWB"
      },
      "source": [
        "### Reduce matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLHhdTn56Ywf",
        "outputId": "6baadfb3-66f3-4a3a-f9aa-29074951f261"
      },
      "source": [
        "w2v_reduced = reduce_matrix(w2v_matrix, variance_treshold=0.01)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INPUT SHAPE: (10000, 1492)\n",
            "OUTPUT SHAPE: (10000, 106)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bimv8Pihzk2"
      },
      "source": [
        "### Save matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hFo-KdhwDUU"
      },
      "source": [
        "with open('/content/drive/MyDrive/nlp/w2v_serie_10000_2.npy', 'wb') as output_file:\n",
        "    np.save(output_file, w2v_reduced)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MV30N4Goh2nf"
      },
      "source": [
        "### Load matrix from storage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "on1CLs95ghfr"
      },
      "source": [
        "w2v_reduced = np.load('/content/drive/MyDrive/nlp/w2v_serie_10000_2.npy')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9svTIM7l1Mm"
      },
      "source": [
        "### Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yu2EI9Ty6osz"
      },
      "source": [
        "def generate_clusters(\n",
        "    matrix: np.ndarray,\n",
        "    n_clusters: int\n",
        ") -> KMeans:\n",
        "    # generate word clusters using the KMeans algorithm.\n",
        "    print(\"\\nClustering started\")\n",
        "    # Instantiate KMeans clusterer for n_clusters\n",
        "    km_model = KMeans(n_clusters=n_clusters, random_state=3)\n",
        "    # create clusters\n",
        "    km_model.fit(matrix)\n",
        "    print(\"Clustering finished\")\n",
        "    return km_model"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehqokty1iTra"
      },
      "source": [
        "### Create clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPhX4J3Z69Hx",
        "outputId": "59baef01-e1c2-4e0e-8724-e88e7b6dfb24"
      },
      "source": [
        "w2v_clusters = generate_clusters(w2v_reduced, 40)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Clustering started\n",
            "Clustering finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsGqDt5jiXko"
      },
      "source": [
        "### Cluster utility functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9m5VxwFj7pvE"
      },
      "source": [
        "def display_summary(clusters: KMeans):\n",
        "    cluster_count = Counter(sorted(clusters.labels_))\n",
        "    for cluster in cluster_count:\n",
        "        print (\"Cluster#\", cluster,\" - Total words:\", cluster_count[cluster])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oN0JpYkuRdp"
      },
      "source": [
        "def annotate_dataframe(clusters: KMeans, df: pd.DataFrame, col_name: str):\n",
        "    cluster_count = Counter(sorted(clusters.labels_))\n",
        "    #sort cluster centers by proximity to centroid\n",
        "    order_centroids = clusters.cluster_centers_.argsort()[:, ::-1] \n",
        "\n",
        "    clusters_df = np.zeros(len(df))\n",
        "    \n",
        "    for cluster_idx in cluster_count:\n",
        "        # get words inside each cluster\n",
        "        cluster_words = np.where(clusters.labels_ == cluster_idx)[0]\n",
        "        # anotate all chats in cluster\n",
        "        for idx in cluster_words:\n",
        "            clusters_df[idx] = int(cluster_idx)\n",
        "\n",
        "    df[col_name] = clusters_df"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVNQmUU0igKL"
      },
      "source": [
        "### Show info about clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSr-_Yk87viz",
        "outputId": "6aa27a7a-94ce-4e40-8cad-371278550940"
      },
      "source": [
        "# show number of words captured by each cluster\n",
        "display_summary(w2v_clusters)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster# 0  - Total words: 6\n",
            "Cluster# 1  - Total words: 1936\n",
            "Cluster# 2  - Total words: 1745\n",
            "Cluster# 3  - Total words: 369\n",
            "Cluster# 4  - Total words: 355\n",
            "Cluster# 5  - Total words: 315\n",
            "Cluster# 6  - Total words: 703\n",
            "Cluster# 7  - Total words: 95\n",
            "Cluster# 8  - Total words: 139\n",
            "Cluster# 9  - Total words: 98\n",
            "Cluster# 10  - Total words: 83\n",
            "Cluster# 11  - Total words: 75\n",
            "Cluster# 12  - Total words: 71\n",
            "Cluster# 13  - Total words: 77\n",
            "Cluster# 14  - Total words: 62\n",
            "Cluster# 15  - Total words: 87\n",
            "Cluster# 16  - Total words: 59\n",
            "Cluster# 17  - Total words: 82\n",
            "Cluster# 18  - Total words: 147\n",
            "Cluster# 19  - Total words: 34\n",
            "Cluster# 20  - Total words: 115\n",
            "Cluster# 21  - Total words: 30\n",
            "Cluster# 22  - Total words: 43\n",
            "Cluster# 23  - Total words: 10\n",
            "Cluster# 24  - Total words: 150\n",
            "Cluster# 25  - Total words: 2448\n",
            "Cluster# 26  - Total words: 5\n",
            "Cluster# 27  - Total words: 196\n",
            "Cluster# 28  - Total words: 4\n",
            "Cluster# 29  - Total words: 38\n",
            "Cluster# 30  - Total words: 11\n",
            "Cluster# 31  - Total words: 151\n",
            "Cluster# 32  - Total words: 4\n",
            "Cluster# 33  - Total words: 1\n",
            "Cluster# 34  - Total words: 16\n",
            "Cluster# 35  - Total words: 23\n",
            "Cluster# 36  - Total words: 11\n",
            "Cluster# 37  - Total words: 177\n",
            "Cluster# 38  - Total words: 14\n",
            "Cluster# 39  - Total words: 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApUleEUNi6NO"
      },
      "source": [
        "### Annotate cluster for each row in dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "628xHBsKvEXw"
      },
      "source": [
        "df_test = df_test.copy()"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5adupRW8v7vk"
      },
      "source": [
        "annotate_dataframe(w2v_clusters, df_test, 'w2v_clusters')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['nwords'] = list(map(lambda x: len(x.split()), df_test['tokens']))"
      ],
      "metadata": {
        "id": "zrh2_G4Gv7bC"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFKi61q3cjhf"
      },
      "source": [
        "df_test.to_csv(f'/content/drive/MyDrive/nlp/w2v_clusters_df_2.csv', index=False)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VD30R-uYgak"
      },
      "source": [
        "### Load Word2Vec clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7h9rfKToYiN8"
      },
      "source": [
        "df_test = pd.read_csv('/content/drive/MyDrive/nlp/w2v_clusters_df_2.csv')"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nycNKOAWjMtN"
      },
      "source": [
        "### Explore results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7jOXlqFw_Lu"
      },
      "source": [
        "w2v_group = df_test.groupby('w2v_clusters')"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpzbiX9uABGx"
      },
      "source": [
        "Shilouette coeficient for this clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CeQz8bN0AHEK",
        "outputId": "f5acd0c2-53cf-44d9-d710-904add1cf33d"
      },
      "source": [
        "metrics.silhouette_score(w2v_reduced, w2v_clusters.labels_, metric='euclidean')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6137541220276498"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OXimEHIjjt1"
      },
      "source": [
        "#### Get toxicity score for each cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIkBmlPAlCoD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "085fd899-28ee-460c-b8a9-bbe0462abf4b"
      },
      "source": [
        "w2v_score = (w2v_group['toxicity'].sum() / w2v_group['nwords'].sum())\n",
        "w2v_scored = pd.DataFrame({'score': w2v_score.values, 'size': w2v_group.size()})\n",
        "w2v_scored = w2v_scored[w2v_scored['score'] > 0.13]\n",
        "w2v_scored = w2v_scored.sort_values(by=['score', 'size'], ascending=[False, False])\n",
        "top_toxic_clusters = w2v_scored.index.to_list()\n",
        "w2v_scored"
      ],
      "execution_count": 53,
      "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>score</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>w2v_clusters</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26.0</th>\n",
              "      <td>0.769231</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23.0</th>\n",
              "      <td>0.611111</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19.0</th>\n",
              "      <td>0.590164</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12.0</th>\n",
              "      <td>0.564286</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22.0</th>\n",
              "      <td>0.557692</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35.0</th>\n",
              "      <td>0.556818</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>0.534884</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28.0</th>\n",
              "      <td>0.533333</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14.0</th>\n",
              "      <td>0.464789</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18.0</th>\n",
              "      <td>0.445070</td>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21.0</th>\n",
              "      <td>0.434783</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37.0</th>\n",
              "      <td>0.429561</td>\n",
              "      <td>177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16.0</th>\n",
              "      <td>0.416149</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11.0</th>\n",
              "      <td>0.405405</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32.0</th>\n",
              "      <td>0.363636</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>0.345196</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13.0</th>\n",
              "      <td>0.341176</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>0.334711</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33.0</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27.0</th>\n",
              "      <td>0.331104</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>0.325532</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>0.324148</td>\n",
              "      <td>369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>0.276657</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 score  size\n",
              "w2v_clusters                \n",
              "26.0          0.769231     5\n",
              "23.0          0.611111    10\n",
              "19.0          0.590164    34\n",
              "12.0          0.564286    71\n",
              "22.0          0.557692    43\n",
              "35.0          0.556818    23\n",
              "30.0          0.534884    11\n",
              "28.0          0.533333     4\n",
              "14.0          0.464789    62\n",
              "18.0          0.445070   147\n",
              "21.0          0.434783    30\n",
              "37.0          0.429561   177\n",
              "16.0          0.416149    59\n",
              "11.0          0.405405    75\n",
              "32.0          0.363636     4\n",
              "7.0           0.345196    95\n",
              "13.0          0.341176    77\n",
              "10.0          0.334711    83\n",
              "33.0          0.333333     1\n",
              "27.0          0.331104   196\n",
              "8.0           0.325532   139\n",
              "3.0           0.324148   369\n",
              "9.0           0.276657    98"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsVbA2HPkeAC"
      },
      "source": [
        "### Explore clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RtpwGTryiitB",
        "outputId": "12b819bd-0931-4f41-d713-366046e79b97"
      },
      "source": [
        "w2v_group.get_group(26)[['match','text']]"
      ],
      "execution_count": 54,
      "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>match</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>87</td>\n",
              "      <td>fuck this shit nap team</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3087</th>\n",
              "      <td>1785</td>\n",
              "      <td>fuck that shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5017</th>\n",
              "      <td>2931</td>\n",
              "      <td>fuck off russians shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5117</th>\n",
              "      <td>2991</td>\n",
              "      <td>fuck this shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7028</th>\n",
              "      <td>3864</td>\n",
              "      <td>fuck this shit</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      match                     text\n",
              "178      87  fuck this shit nap team\n",
              "3087   1785           fuck that shit\n",
              "5017   2931   fuck off russians shit\n",
              "5117   2991           fuck this shit\n",
              "7028   3864           fuck this shit"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hni6Z8kbZHyS",
        "outputId": "65024163-f4f3-432b-ad9c-98ed2fb52a96"
      },
      "source": [
        "w2v_group.get_group(23)[['match','text']]"
      ],
      "execution_count": 55,
      "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>match</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1120</th>\n",
              "      <td>636</td>\n",
              "      <td>so fucking noisy but just a piece of shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2381</th>\n",
              "      <td>1421</td>\n",
              "      <td>fucking lucky shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3109</th>\n",
              "      <td>1795</td>\n",
              "      <td>fucking shit ass lion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3407</th>\n",
              "      <td>1988</td>\n",
              "      <td>we did evry fucking shit for him</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6555</th>\n",
              "      <td>3662</td>\n",
              "      <td>fucking trash russian shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7066</th>\n",
              "      <td>3887</td>\n",
              "      <td>3 fucking piece of shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7257</th>\n",
              "      <td>3974</td>\n",
              "      <td>fucking monkey shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7713</th>\n",
              "      <td>4234</td>\n",
              "      <td>fucking piece of shit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7768</th>\n",
              "      <td>4253</td>\n",
              "      <td>you play 2 mid and call us try hard you fuckin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8619</th>\n",
              "      <td>4672</td>\n",
              "      <td>fucking retarded shit</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      match                                               text\n",
              "1120    636          so fucking noisy but just a piece of shit\n",
              "2381   1421                                 fucking lucky shit\n",
              "3109   1795                              fucking shit ass lion\n",
              "3407   1988                   we did evry fucking shit for him\n",
              "6555   3662                         fucking trash russian shit\n",
              "7066   3887                            3 fucking piece of shit\n",
              "7257   3974                                fucking monkey shit\n",
              "7713   4234                              fucking piece of shit\n",
              "7768   4253  you play 2 mid and call us try hard you fuckin...\n",
              "8619   4672                              fucking retarded shit"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2ItS34m3ZMsl",
        "outputId": "38b4434f-b7a2-409f-f90f-8e6bdb627d69"
      },
      "source": [
        "w2v_group.get_group(19)[['match','text']]"
      ],
      "execution_count": 56,
      "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>match</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>129</td>\n",
              "      <td>but my teammates wont to kill u((9(</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1122</th>\n",
              "      <td>636</td>\n",
              "      <td>u go kill then quit?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1145</th>\n",
              "      <td>671</td>\n",
              "      <td>and u didnt kill me its necro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>779</td>\n",
              "      <td>always kill supp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1609</th>\n",
              "      <td>1002</td>\n",
              "      <td>I won0't kill you</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1878</th>\n",
              "      <td>1153</td>\n",
              "      <td>can only can kill me with ur 2 bodyguards</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>1249</td>\n",
              "      <td>you kill top[</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2163</th>\n",
              "      <td>1327</td>\n",
              "      <td>kill this monkey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2213</th>\n",
              "      <td>1345</td>\n",
              "      <td>how i kill all of you</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2323</th>\n",
              "      <td>1395</td>\n",
              "      <td>or again kill you</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2343</th>\n",
              "      <td>1405</td>\n",
              "      <td>unpause and kill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3034</th>\n",
              "      <td>1751</td>\n",
              "      <td>Kill urself wywern picker</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3247</th>\n",
              "      <td>1877</td>\n",
              "      <td>didnt even get the kill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3346</th>\n",
              "      <td>1955</td>\n",
              "      <td>gj. I can't kill you all</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3462</th>\n",
              "      <td>2010</td>\n",
              "      <td>if you kill me its racist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3471</th>\n",
              "      <td>2015</td>\n",
              "      <td>he kill himself 4 minu ago</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3930</th>\n",
              "      <td>2347</td>\n",
              "      <td>just go kill him</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4533</th>\n",
              "      <td>2659</td>\n",
              "      <td>u kill him so much</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4641</th>\n",
              "      <td>2743</td>\n",
              "      <td>so u wont kill me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4924</th>\n",
              "      <td>2876</td>\n",
              "      <td>goo kill him I will not save him</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5337</th>\n",
              "      <td>3095</td>\n",
              "      <td>see cant even kill zeys</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5719</th>\n",
              "      <td>3299</td>\n",
              "      <td>did you level off that kill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6952</th>\n",
              "      <td>3823</td>\n",
              "      <td>for a triple light kill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7581</th>\n",
              "      <td>4140</td>\n",
              "      <td>can i get an aghnims? so your es can't kill me ck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7873</th>\n",
              "      <td>4284</td>\n",
              "      <td>Because you didnt kill me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7920</th>\n",
              "      <td>4301</td>\n",
              "      <td>you should kill me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7966</th>\n",
              "      <td>4304</td>\n",
              "      <td>i kill you pa and mag whatch me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8606</th>\n",
              "      <td>4661</td>\n",
              "      <td>and you coulkn't even kill me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8636</th>\n",
              "      <td>4677</td>\n",
              "      <td>why you kill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9009</th>\n",
              "      <td>4880</td>\n",
              "      <td>when i had chancse to kill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9144</th>\n",
              "      <td>4936</td>\n",
              "      <td>kill meif u can bitches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9232</th>\n",
              "      <td>5004</td>\n",
              "      <td>u cant kill me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9586</th>\n",
              "      <td>5202</td>\n",
              "      <td>yaa For me to kill?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9784</th>\n",
              "      <td>5333</td>\n",
              "      <td>i will kill myself</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      match                                               text\n",
              "279     129                but my teammates wont to kill u((9(\n",
              "1122    636                              u go kill then quit? \n",
              "1145    671                      and u didnt kill me its necro\n",
              "1311    779                                   always kill supp\n",
              "1609   1002                                  I won0't kill you\n",
              "1878   1153          can only can kill me with ur 2 bodyguards\n",
              "2019   1249                                      you kill top[\n",
              "2163   1327                                   kill this monkey\n",
              "2213   1345                              how i kill all of you\n",
              "2323   1395                                  or again kill you\n",
              "2343   1405                                   unpause and kill\n",
              "3034   1751                          Kill urself wywern picker\n",
              "3247   1877                           didnt even get the kill \n",
              "3346   1955                           gj. I can't kill you all\n",
              "3462   2010                          if you kill me its racist\n",
              "3471   2015                         he kill himself 4 minu ago\n",
              "3930   2347                                   just go kill him\n",
              "4533   2659                                 u kill him so much\n",
              "4641   2743                                  so u wont kill me\n",
              "4924   2876                   goo kill him I will not save him\n",
              "5337   3095                            see cant even kill zeys\n",
              "5719   3299                        did you level off that kill\n",
              "6952   3823                            for a triple light kill\n",
              "7581   4140  can i get an aghnims? so your es can't kill me ck\n",
              "7873   4284                          Because you didnt kill me\n",
              "7920   4301                                 you should kill me\n",
              "7966   4304                    i kill you pa and mag whatch me\n",
              "8606   4661                      and you coulkn't even kill me\n",
              "8636   4677                                       why you kill\n",
              "9009   4880                         when i had chancse to kill\n",
              "9144   4936                            kill meif u can bitches\n",
              "9232   5004                                     u cant kill me\n",
              "9586   5202                                yaa For me to kill?\n",
              "9784   5333                                 i will kill myself"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "De6eY6ArZQzj",
        "outputId": "e759078d-51f6-409a-c506-0fc5b4c2182a"
      },
      "source": [
        "w2v_group.get_group(12)[['match','text']]"
      ],
      "execution_count": 57,
      "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>match</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>82</td>\n",
              "      <td>Ima out to fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>107</td>\n",
              "      <td>i fuck your mum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>121</td>\n",
              "      <td>what the fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>121</td>\n",
              "      <td>what the fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>153</td>\n",
              "      <td>where the fuck is snow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450</th>\n",
              "      <td>227</td>\n",
              "      <td>fuck you buyer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>722</th>\n",
              "      <td>378</td>\n",
              "      <td>cant fuck with oracle!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1099</th>\n",
              "      <td>612</td>\n",
              "      <td>get the fuck out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1364</th>\n",
              "      <td>826</td>\n",
              "      <td>fuck off dudes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1639</th>\n",
              "      <td>1033</td>\n",
              "      <td>bc fuck u thats y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1799</th>\n",
              "      <td>1122</td>\n",
              "      <td>what the fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1845</th>\n",
              "      <td>1126</td>\n",
              "      <td>how the fuck is this balance ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2043</th>\n",
              "      <td>1256</td>\n",
              "      <td>chrono me for what u fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2056</th>\n",
              "      <td>1260</td>\n",
              "      <td>fuck your moms</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2088</th>\n",
              "      <td>1274</td>\n",
              "      <td>fuck that necro he sucks anyways</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2403</th>\n",
              "      <td>1437</td>\n",
              "      <td>waif fuck your beloved oness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3006</th>\n",
              "      <td>1741</td>\n",
              "      <td>can you fuck off ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3223</th>\n",
              "      <td>1859</td>\n",
              "      <td>fuck that dark willow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3244</th>\n",
              "      <td>1877</td>\n",
              "      <td>alright, fuck you kunkka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3245</th>\n",
              "      <td>1877</td>\n",
              "      <td>again.. fuck you kunkka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3270</th>\n",
              "      <td>1890</td>\n",
              "      <td>what the fuck?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3287</th>\n",
              "      <td>1898</td>\n",
              "      <td>17minutes fuck off rd??</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3337</th>\n",
              "      <td>1945</td>\n",
              "      <td>fuck your family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3411</th>\n",
              "      <td>1991</td>\n",
              "      <td>how the fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3466</th>\n",
              "      <td>2015</td>\n",
              "      <td>go fuck off somewhere else</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3563</th>\n",
              "      <td>2044</td>\n",
              "      <td>will fuck you again bird</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3637</th>\n",
              "      <td>2083</td>\n",
              "      <td>what the fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3741</th>\n",
              "      <td>2174</td>\n",
              "      <td>fuck me when i  save this moron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3760</th>\n",
              "      <td>2201</td>\n",
              "      <td>the fuck is nam nam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3904</th>\n",
              "      <td>2328</td>\n",
              "      <td>the fuck are you doing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3965</th>\n",
              "      <td>2361</td>\n",
              "      <td>fuck off already</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4105</th>\n",
              "      <td>2436</td>\n",
              "      <td>why the fuck u tped</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4113</th>\n",
              "      <td>2440</td>\n",
              "      <td>fuck all the niggas'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4148</th>\n",
              "      <td>2458</td>\n",
              "      <td>i did not fuck your gf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4332</th>\n",
              "      <td>2548</td>\n",
              "      <td>go fuck yourself bick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4411</th>\n",
              "      <td>2596</td>\n",
              "      <td>can u call him a fat fuck for me?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4506</th>\n",
              "      <td>2646</td>\n",
              "      <td>idk how the fuck he feeds so much</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4682</th>\n",
              "      <td>2768</td>\n",
              "      <td>the fuck  you doing there</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4795</th>\n",
              "      <td>2803</td>\n",
              "      <td>fuck you all</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5018</th>\n",
              "      <td>2931</td>\n",
              "      <td>fuck off and act mature</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5022</th>\n",
              "      <td>2931</td>\n",
              "      <td>fuck you all</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5535</th>\n",
              "      <td>3204</td>\n",
              "      <td>fuck you rat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5727</th>\n",
              "      <td>3302</td>\n",
              "      <td>fuck outta here</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6241</th>\n",
              "      <td>3535</td>\n",
              "      <td>fuck this cxarry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6411</th>\n",
              "      <td>3599</td>\n",
              "      <td>i have places to be and bitches to fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6445</th>\n",
              "      <td>3622</td>\n",
              "      <td>saved you lil' fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6725</th>\n",
              "      <td>3722</td>\n",
              "      <td>fuck off brah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6849</th>\n",
              "      <td>3762</td>\n",
              "      <td>fuck you  kotl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6851</th>\n",
              "      <td>3762</td>\n",
              "      <td>fuck you kotl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7409</th>\n",
              "      <td>4071</td>\n",
              "      <td>fuck off weab</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7439</th>\n",
              "      <td>4088</td>\n",
              "      <td>fuck the courier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7620</th>\n",
              "      <td>4164</td>\n",
              "      <td>fuck you bug</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7712</th>\n",
              "      <td>4228</td>\n",
              "      <td>Fuck you Wes POS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7922</th>\n",
              "      <td>4304</td>\n",
              "      <td>i only fuck muslim girls</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7939</th>\n",
              "      <td>4304</td>\n",
              "      <td>i fuck your sister magnus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7942</th>\n",
              "      <td>4304</td>\n",
              "      <td>i only fuck muslim girls</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7954</th>\n",
              "      <td>4304</td>\n",
              "      <td>i fuck gigi hadid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8053</th>\n",
              "      <td>4355</td>\n",
              "      <td>fuck you heart</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8370</th>\n",
              "      <td>4541</td>\n",
              "      <td>fuck rsa unranked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8667</th>\n",
              "      <td>4702</td>\n",
              "      <td>the fuck are you doing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8893</th>\n",
              "      <td>4813</td>\n",
              "      <td>i did fuck your mum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8906</th>\n",
              "      <td>4820</td>\n",
              "      <td>fuck you nimnko</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8941</th>\n",
              "      <td>4844</td>\n",
              "      <td>who the fuck cares</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9174</th>\n",
              "      <td>4962</td>\n",
              "      <td>who can fuck me？</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9302</th>\n",
              "      <td>5030</td>\n",
              "      <td>i will fuck u too now</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9414</th>\n",
              "      <td>5095</td>\n",
              "      <td>fuck the kids</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9675</th>\n",
              "      <td>5240</td>\n",
              "      <td>fuck key board</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9701</th>\n",
              "      <td>5250</td>\n",
              "      <td>wake the fuck up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9741</th>\n",
              "      <td>5295</td>\n",
              "      <td>fuck you nigg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9814</th>\n",
              "      <td>5336</td>\n",
              "      <td>fuck off tusk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9962</th>\n",
              "      <td>5395</td>\n",
              "      <td>fuck you sniepr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      match                                     text\n",
              "131      82                          Ima out to fuck\n",
              "203     107                          i fuck your mum\n",
              "235     121                            what the fuck\n",
              "244     121                            what the fuck\n",
              "307     153                   where the fuck is snow\n",
              "450     227                          fuck you buyer \n",
              "722     378                   cant fuck with oracle!\n",
              "1099    612                         get the fuck out\n",
              "1364    826                           fuck off dudes\n",
              "1639   1033                        bc fuck u thats y\n",
              "1799   1122                            what the fuck\n",
              "1845   1126           how the fuck is this balance ?\n",
              "2043   1256                chrono me for what u fuck\n",
              "2056   1260                           fuck your moms\n",
              "2088   1274         fuck that necro he sucks anyways\n",
              "2403   1437             waif fuck your beloved oness\n",
              "3006   1741                       can you fuck off ?\n",
              "3223   1859                    fuck that dark willow\n",
              "3244   1877                 alright, fuck you kunkka\n",
              "3245   1877                  again.. fuck you kunkka\n",
              "3270   1890                           what the fuck?\n",
              "3287   1898                  17minutes fuck off rd??\n",
              "3337   1945                         fuck your family\n",
              "3411   1991                             how the fuck\n",
              "3466   2015               go fuck off somewhere else\n",
              "3563   2044                 will fuck you again bird\n",
              "3637   2083                            what the fuck\n",
              "3741   2174          fuck me when i  save this moron\n",
              "3760   2201                      the fuck is nam nam\n",
              "3904   2328                   the fuck are you doing\n",
              "3965   2361                         fuck off already\n",
              "4105   2436                      why the fuck u tped\n",
              "4113   2440                     fuck all the niggas'\n",
              "4148   2458                   i did not fuck your gf\n",
              "4332   2548                    go fuck yourself bick\n",
              "4411   2596        can u call him a fat fuck for me?\n",
              "4506   2646        idk how the fuck he feeds so much\n",
              "4682   2768                the fuck  you doing there\n",
              "4795   2803                             fuck you all\n",
              "5018   2931                  fuck off and act mature\n",
              "5022   2931                             fuck you all\n",
              "5535   3204                             fuck you rat\n",
              "5727   3302                          fuck outta here\n",
              "6241   3535                         fuck this cxarry\n",
              "6411   3599  i have places to be and bitches to fuck\n",
              "6445   3622                      saved you lil' fuck\n",
              "6725   3722                            fuck off brah\n",
              "6849   3762                           fuck you  kotl\n",
              "6851   3762                            fuck you kotl\n",
              "7409   4071                            fuck off weab\n",
              "7439   4088                         fuck the courier\n",
              "7620   4164                             fuck you bug\n",
              "7712   4228                         Fuck you Wes POS\n",
              "7922   4304                 i only fuck muslim girls\n",
              "7939   4304                i fuck your sister magnus\n",
              "7942   4304                 i only fuck muslim girls\n",
              "7954   4304                        i fuck gigi hadid\n",
              "8053   4355                           fuck you heart\n",
              "8370   4541                        fuck rsa unranked\n",
              "8667   4702                   the fuck are you doing\n",
              "8893   4813                      i did fuck your mum\n",
              "8906   4820                          fuck you nimnko\n",
              "8941   4844                       who the fuck cares\n",
              "9174   4962                         who can fuck me？\n",
              "9302   5030                    i will fuck u too now\n",
              "9414   5095                            fuck the kids\n",
              "9675   5240                           fuck key board\n",
              "9701   5250                         wake the fuck up\n",
              "9741   5295                            fuck you nigg\n",
              "9814   5336                            fuck off tusk\n",
              "9962   5395                          fuck you sniepr"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ELuSBIU7ZTqq",
        "outputId": "da52045d-b583-4259-a529-65571f6c8821"
      },
      "source": [
        "w2v_group.get_group(22)[['match', 'text']]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>match</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>111</td>\n",
              "      <td>fucking hard game ever when got lucky timing s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>121</td>\n",
              "      <td>ur mom is fucking slut</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>121</td>\n",
              "      <td>are fucking retard ursa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>290</td>\n",
              "      <td>this lineup is fucking stupid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>657</th>\n",
              "      <td>339</td>\n",
              "      <td>retard my fucking team</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1014</th>\n",
              "      <td>554</td>\n",
              "      <td>fucking idiot offlaner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>598</td>\n",
              "      <td>report necro and fucking riki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>683</td>\n",
              "      <td>jugg you fucking moron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1182</th>\n",
              "      <td>702</td>\n",
              "      <td>go fucking kill him</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1832</th>\n",
              "      <td>1125</td>\n",
              "      <td>Already pick first invoker and i playing my ro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2074</th>\n",
              "      <td>1269</td>\n",
              "      <td>fucking retarded russian dogs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2166</th>\n",
              "      <td>1327</td>\n",
              "      <td>fucking monkey suck my dick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2234</th>\n",
              "      <td>1353</td>\n",
              "      <td>fucking asshole mid more</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2250</th>\n",
              "      <td>1364</td>\n",
              "      <td>this pudge so fucking stupid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2355</th>\n",
              "      <td>1407</td>\n",
              "      <td>this invo so fucking asshole</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2654</th>\n",
              "      <td>1540</td>\n",
              "      <td>u fucking selfish prick qop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2775</th>\n",
              "      <td>1621</td>\n",
              "      <td>you fucking suck wk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2895</th>\n",
              "      <td>1710</td>\n",
              "      <td>fucking pussy team man</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3272</th>\n",
              "      <td>1890</td>\n",
              "      <td>fucking try harder</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3916</th>\n",
              "      <td>2343</td>\n",
              "      <td>? ur a bristle you fucking retard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4214</th>\n",
              "      <td>2484</td>\n",
              "      <td>fucking idiot mid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4448</th>\n",
              "      <td>2623</td>\n",
              "      <td>fucking idiot team</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5010</th>\n",
              "      <td>2917</td>\n",
              "      <td>it is 5v5 game u fucking cunt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5197</th>\n",
              "      <td>3018</td>\n",
              "      <td>i fucking kill you</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5483</th>\n",
              "      <td>3177</td>\n",
              "      <td>end this is bulshit gaming fucking sniper and ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5519</th>\n",
              "      <td>3188</td>\n",
              "      <td>fucking clueless fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5974</th>\n",
              "      <td>3422</td>\n",
              "      <td>why u fucking hounting kills just push</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6472</th>\n",
              "      <td>3629</td>\n",
              "      <td>fucking kill yourself</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6740</th>\n",
              "      <td>3725</td>\n",
              "      <td>fucking god can't give less than 4 secs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6793</th>\n",
              "      <td>3748</td>\n",
              "      <td>team is fucking stupid animals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7082</th>\n",
              "      <td>3894</td>\n",
              "      <td>why i have this fucking retarded russian in my...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7101</th>\n",
              "      <td>3896</td>\n",
              "      <td>just now fucking idiot naix and ck  say me wil...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7168</th>\n",
              "      <td>3911</td>\n",
              "      <td>it is fucking unranked chill the fuck out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7781</th>\n",
              "      <td>4255</td>\n",
              "      <td>dude if you're gonna bitch this much then dont...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7835</th>\n",
              "      <td>4265</td>\n",
              "      <td>CAmila fucking bitch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7924</th>\n",
              "      <td>4304</td>\n",
              "      <td>shut up u fucking virign cunt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7938</th>\n",
              "      <td>4304</td>\n",
              "      <td>fucking indian fuck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8057</th>\n",
              "      <td>4366</td>\n",
              "      <td>Fucking Russians retard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8582</th>\n",
              "      <td>4640</td>\n",
              "      <td>just fucking kill me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8763</th>\n",
              "      <td>4754</td>\n",
              "      <td>that is fucking bitch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9613</th>\n",
              "      <td>5216</td>\n",
              "      <td>easy fucking slave</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9681</th>\n",
              "      <td>5241</td>\n",
              "      <td>that fucking ant is dead so im happy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9974</th>\n",
              "      <td>5396</td>\n",
              "      <td>you are fucking idiot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      match                                               text\n",
              "215     111  fucking hard game ever when got lucky timing s...\n",
              "241     121                             ur mom is fucking slut\n",
              "242     121                            are fucking retard ursa\n",
              "548     290                      this lineup is fucking stupid\n",
              "657     339                            retard my fucking team \n",
              "1014    554                             fucking idiot offlaner\n",
              "1089    598                      report necro and fucking riki\n",
              "1156    683                             jugg you fucking moron\n",
              "1182    702                                go fucking kill him\n",
              "1832   1125  Already pick first invoker and i playing my ro...\n",
              "2074   1269                      fucking retarded russian dogs\n",
              "2166   1327                        fucking monkey suck my dick\n",
              "2234   1353                           fucking asshole mid more\n",
              "2250   1364                       this pudge so fucking stupid\n",
              "2355   1407                       this invo so fucking asshole\n",
              "2654   1540                        u fucking selfish prick qop\n",
              "2775   1621                                you fucking suck wk\n",
              "2895   1710                             fucking pussy team man\n",
              "3272   1890                                 fucking try harder\n",
              "3916   2343                  ? ur a bristle you fucking retard\n",
              "4214   2484                                  fucking idiot mid\n",
              "4448   2623                                 fucking idiot team\n",
              "5010   2917                      it is 5v5 game u fucking cunt\n",
              "5197   3018                                 i fucking kill you\n",
              "5483   3177  end this is bulshit gaming fucking sniper and ...\n",
              "5519   3188                              fucking clueless fuck\n",
              "5974   3422             why u fucking hounting kills just push\n",
              "6472   3629                              fucking kill yourself\n",
              "6740   3725            fucking god can't give less than 4 secs\n",
              "6793   3748                    team is fucking stupid animals \n",
              "7082   3894  why i have this fucking retarded russian in my...\n",
              "7101   3896  just now fucking idiot naix and ck  say me wil...\n",
              "7168   3911          it is fucking unranked chill the fuck out\n",
              "7781   4255  dude if you're gonna bitch this much then dont...\n",
              "7835   4265                              CAmila fucking bitch \n",
              "7924   4304                      shut up u fucking virign cunt\n",
              "7938   4304                                fucking indian fuck\n",
              "8057   4366                            Fucking Russians retard\n",
              "8582   4640                              just fucking kill me \n",
              "8763   4754                              that is fucking bitch\n",
              "9613   5216                                 easy fucking slave\n",
              "9681   5241               that fucking ant is dead so im happy\n",
              "9974   5396                              you are fucking idiot"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMUzAcFgZVxD",
        "outputId": "1511937a-6d7e-4457-cab6-147421c68112"
      },
      "source": [
        "w2v_group.get_group(35)['text']"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54                       yuou just fuck your ulti as hell\n",
              "875                           they no skill fear the fuck\n",
              "1758                                 die fuck your mother\n",
              "2325                               shut the fuck up bitch\n",
              "3290                            fuck off mid farm and die\n",
              "3502    hey faggot ass woo ima show your stupid fuck h...\n",
              "3503                            nah fuck that toxic cunt \n",
              "3516                                        fuck that kid\n",
              "3915                                shut the fuck up cunt\n",
              "5175            how the fuck can you deny xp to a sniper?\n",
              "5585    why the fuck can you doom me during shadow realm?\n",
              "6198    merry christmass kid picks morph even though h...\n",
              "6788    he saw a sniper in enemy team and he say fuck ...\n",
              "7107                                    injoker noob fuck\n",
              "7489                                  fuck you mom retard\n",
              "7590                          dont fuck with my bas bitch\n",
              "7937                                 fuck u am u dog cunt\n",
              "7943                                 fuck am so noob guys\n",
              "7962                                     ure noob as fuck\n",
              "8744                                     fuck you mom ass\n",
              "8791               your team is gay as fuck, jugga cleave\n",
              "9175    Lovesbeing killed for the second time on 800 m...\n",
              "9810                              ok kill yourself u fuck\n",
              "Name: text, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuAhCkl54oFa"
      },
      "source": [
        "### Reduce dimensionality to display data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2eXuWZ32kgeb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d12aeca-8129-4178-8475-e713ad7e2558"
      },
      "source": [
        "# use TSNE to reduce dimensionality of data\n",
        "tsne = sklearn.manifold.TSNE(n_components=2, random_state=0)\n",
        "matrix_dicc2d = tsne.fit_transform(w2v_reduced)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etSq6Apt41aF"
      },
      "source": [
        "### Display data without clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_piuCSXj42gl"
      },
      "source": [
        "pointsspacy = pd.DataFrame(\n",
        "    [(chat, coord[0], coord[1]) for (chat, coord) in zip(chats, matrix_dicc2d)],\n",
        "    columns=[\"word\", \"x\", \"y\"]\n",
        ")"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esuzqLxz5B4i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "62cfef7b-e013-422a-a5bf-76a753bbd428"
      },
      "source": [
        "pointsspacy.plot.scatter(\"x\", \"y\", s=10, figsize=(20, 12))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f63c80b17d0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQ0zlijU5IYf"
      },
      "source": [
        "### Display clusters and clusters with biggest proportion of toxic words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "6MQ6u2oC5JQk",
        "outputId": "8d8b3baf-027e-4250-fc26-17217fbea3ea"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "pointscluster = pd.DataFrame(\n",
        "    [(chat, coord[0], coord[1], cluster) for (chat, coord, cluster) in zip(df_test['clean'], matrix_dicc2d, df_test['w2v_clusters']) ],\n",
        "    columns=[\"word\", \"x\", \"y\", \"c\"]\n",
        ")\n",
        "pointscluster.plot(ax=ax, kind='scatter', x='x', y='y', c='c', cmap='viridis', s=10, figsize=(20, 12))\n",
        "toxic_clusters = pd.DataFrame(\n",
        "    [(coord[0], coord[1]) for (coord, cluster) in zip(matrix_dicc2d, df_test['w2v_clusters']) if cluster in top_toxic_clusters ],\n",
        "    columns=[\"x\", \"y\"]\n",
        ")\n",
        "toxic_clusters.plot(ax=ax, kind='scatter', x='x', y='y', color='red')\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}