NFT_NLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0dd0bcf-f562-4381-9e22-76f31b59fc8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import NMF, TruncatedSVD\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd7cb5a7-8a57-4b5a-8afb-6bb4f464168a",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be7538ba-55c7-4431-aeb0-88e65d073298",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: DtypeWarning: Columns (0,1,2,3,4,6,7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('the-reddit-nft-dataset-comments.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "879465e6-9e8e-473c-92b3-ac5ac454174a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data preprocessing\n",
    "data_set = df.sample(n=1000000,random_state = 50).dropna().drop(columns=['id','subreddit.id','subreddit.name','subreddit.nsfw','created_utc','type','sentiment','score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa879825-2e9c-44e9-8fa4-96e944ec7d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_dup(string):\n",
    "    new_string = \"\"\n",
    "    prev_string = \"\"\n",
    "    for char in string:\n",
    "        if len(new_string) == 0:\n",
    "            new_string += char\n",
    "            prev_string = char\n",
    "        if char == prev_string:\n",
    "            continue\n",
    "        else:\n",
    "            new_string += char\n",
    "            prev_string = char\n",
    "    return new_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86dcfc31-0b02-4acb-befd-f83b68b6964b",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphanumeric = lambda x: re.sub(\"[^a-zA-Z0-9 ]\",'',x)\n",
    "http = lambda x: re.sub('http\\S+', '',x)\n",
    "lower_case = lambda x: x.lower()\n",
    "duplicates = lambda x: remove_dup(x)\n",
    "all_nums = lambda x: re.sub(\"\\d+\", '', x)\n",
    "nft = lambda x: re.sub(\"nft\\S+\",'',x)\n",
    "discord = lambda x: re.sub(\"discord\\S+\",'',x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ecf2f94-282a-4c3b-a8e5-ebad8e1a90ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set['body'] = data_set.body.map(duplicates).map(alphanumeric).map(http).map(all_nums).map(nft).map(discord).map(lower_case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ea69e20-65cc-49bb-9d91-08f5eeae8a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create copy of dataset\n",
    "analysis = data_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a780bf06-7d4e-4158-92d6-bfc0244e55c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276282, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3746e4ba-917c-4e93-a0ff-b2830a174749",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add new stop words to the nltk list\n",
    "cus_stop_words = nltk.corpus.stopwords.words('english')\n",
    "custom_stop = ['nft','fungible','token','nfts','htps','com','amp','gt','coments','get','go','cointest','fresh','polydroids',\n",
    "               'twiter','ur','mad','stay','thebevyvip','wil', 'like', 'al', 'people', 'based', 'date', 'gras', 'would', 'one',\n",
    "              'gras', 'kevin', 'based','fuck','old','sort','nbsp','think', 'please', 'directly', 'showing', \n",
    "               'life', 'away', 'start','rnft','join']\n",
    "cus_stop_words.extend(custom_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c91e5a0-2772-4a6e-b2b8-79f55be2ecd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cus_stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0312d80a-0e8a-4ab6-98e9-dfac9f8fd7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_topics(model, feature_names, no_top_words, topic_names=None):\n",
    "    for x, topic in enumerate(model.components_):\n",
    "        if not topic_names or not topic_names[x]:\n",
    "            print(\"\\nTopic \", x)\n",
    "        else:\n",
    "            print(\"\\nTopic: '\",topic_names[x],\"'\")\n",
    "        print(\", \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]))\n",
    "        print(\"\\n\")\n",
    "    return model, feature_names, no_top_words\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad6edc5-92ce-491d-b9e9-ef4ef6890e51",
   "metadata": {},
   "source": [
    "## Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3f385bae-328d-424a-8564-64d7582e969a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276282, 218995)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(stop_words=cus_stop_words,\n",
    "                      max_df=0.6)\n",
    "\n",
    "terms_vec = vectorizer.fit_transform(analysis.body)\n",
    "\n",
    "terms_vec.toarray()\n",
    "\n",
    "terms_vec.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ca8bd560-b362-4536-a4aa-80940d6b2809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_inertia = []\n",
    "count_num_clusters = list(range(1,15))\n",
    "for x in count_num_clusters:\n",
    "    km = KMeans(n_clusters=x)\n",
    "    km.fit(terms_vec)\n",
    "    count_inertia.append(km.inertia_)\n",
    "    \n",
    "plt.plot(count_num_clusters,count_inertia)\n",
    "plt.scatter(count_num_clusters,count_inertia)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c33dc9b8-e64f-491e-b251-0598e3340a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa</th>\n",
       "      <th>aaa</th>\n",
       "      <th>aachtchualyi</th>\n",
       "      <th>aachtshualeyh</th>\n",
       "      <th>aada</th>\n",
       "      <th>aadafinancehtpstwitercomaadafinance</th>\n",
       "      <th>aadhar</th>\n",
       "      <th>aaevegotchi</th>\n",
       "      <th>aah</th>\n",
       "      <th>aaiflavored</th>\n",
       "      <th>...</th>\n",
       "      <th>zyngier</th>\n",
       "      <th>zyns</th>\n",
       "      <th>zyori</th>\n",
       "      <th>zytara</th>\n",
       "      <th>zyxwvuhtpswreditcomuserzyxwvu</th>\n",
       "      <th>zyz</th>\n",
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       "    <tr>\n",
       "      <th>276280</th>\n",
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       "      <th>276281</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276282 rows × 218995 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        aa  aaa  aachtchualyi  aachtshualeyh  aada  \\\n",
       "0        0    0             0              0     0   \n",
       "1        0    0             0              0     0   \n",
       "2        0    0             0              0     0   \n",
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       "4        0    0             0              0     0   \n",
       "...     ..  ...           ...            ...   ...   \n",
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       "276280   0    0             0              0     0   \n",
       "276281   0    0             0              0     0   \n",
       "\n",
       "        aadafinancehtpstwitercomaadafinance  aadhar  aaevegotchi  aah  \\\n",
       "0                                         0       0            0    0   \n",
       "1                                         0       0            0    0   \n",
       "2                                         0       0            0    0   \n",
       "3                                         0       0            0    0   \n",
       "4                                         0       0            0    0   \n",
       "...                                     ...     ...          ...  ...   \n",
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       "276280                                    0       0            0    0   \n",
       "276281                                    0       0            0    0   \n",
       "\n",
       "        aaiflavored  ...  zyngier  zyns  zyori  zytara  \\\n",
       "0                 0  ...        0     0      0       0   \n",
       "1                 0  ...        0     0      0       0   \n",
       "2                 0  ...        0     0      0       0   \n",
       "3                 0  ...        0     0      0       0   \n",
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       "...             ...  ...      ...   ...    ...     ...   \n",
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       "276280            0  ...        0     0      0       0   \n",
       "276281            0  ...        0     0      0       0   \n",
       "\n",
       "        zyxwvuhtpswreditcomuserzyxwvu  zyz  zz  zzeung  zzidog  zzzzz  \n",
       "0                                   0    0   0       0       0      0  \n",
       "1                                   0    0   0       0       0      0  \n",
       "2                                   0    0   0       0       0      0  \n",
       "3                                   0    0   0       0       0      0  \n",
       "4                                   0    0   0       0       0      0  \n",
       "...                               ...  ...  ..     ...     ...    ...  \n",
       "276277                              0    0   0       0       0      0  \n",
       "276278                              0    0   0       0       0      0  \n",
       "276279                              0    0   0       0       0      0  \n",
       "276280                              0    0   0       0       0      0  \n",
       "276281                              0    0   0       0       0      0  \n",
       "\n",
       "[276282 rows x 218995 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec_df = pd.DataFrame(terms_vec.toarray(), columns=vectorizer.get_feature_names_out())\n",
    "vec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1e872122-6fde-422a-bff1-8b67c3a0b1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TruncatedSVD(n_components=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TruncatedSVD</label><div class=\"sk-toggleable__content\"><pre>TruncatedSVD(n_components=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TruncatedSVD(n_components=10)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsa = TruncatedSVD(10)\n",
    "lsa.fit(terms_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e9a5db96-010c-417c-98a6-b38d050325ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "cum, cumc, cumcum, belgium, taiwan, fortnite, meow, koala, zamn, china\n",
      "\n",
      "\n",
      "\n",
      "Topic  1\n",
      "discordhtps, automaticaly, questions, bot, concerns, action, moderators, contact, performed, submision\n",
      "\n",
      "\n",
      "\n",
      "Topic  2\n",
      "click, models, make, money, learn, onlyfans, new, bevy, content, sexy\n",
      "\n",
      "\n",
      "\n",
      "Topic  3\n",
      "meow, fortnite, taiwan, belgium, koala, china, zamn, femboy, amogus, cat\n",
      "\n",
      "\n",
      "\n",
      "Topic  4\n",
      "meow, taiwan, belgium, koala, fortnite, zamn, femboy, china, amogus, models\n",
      "\n",
      "\n",
      "\n",
      "Topic  5\n",
      "youre, dont, cant, ratio, cringe, care, yo, sex, bitches, baka\n",
      "\n",
      "\n",
      "\n",
      "Topic  6\n",
      "subredit, gme, jan, money, im, everything, even, could, value, thats\n",
      "\n",
      "\n",
      "\n",
      "Topic  7\n",
      "subredit, gme, jan, fri, thu, mar, wed, sold, feb, shares\n",
      "\n",
      "\n",
      "\n",
      "Topic  8\n",
      "us, live, everything, discusion, buysel, thank, game, discordhtps, share, dont\n",
      "\n",
      "\n",
      "\n",
      "Topic  9\n",
      "game, games, subredit, play, earn, jan, gme, playtoearn, gaming, playing\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vec_topics = show_topics(lsa, vectorizer.get_feature_names_out(), 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ed57a5-2a33-40f0-ba87-df05c3a3eae5",
   "metadata": {},
   "source": [
    "## TFIDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6fbba081-33b0-44a9-ad24-5bec57f0250c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276282, 218995)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF = TfidfVectorizer(stop_words=cus_stop_words,\n",
    "                      max_df=0.4)\n",
    "\n",
    "terms_TFIDF = TFIDF.fit_transform(analysis.body)\n",
    "\n",
    "terms_TFIDF.toarray()\n",
    "\n",
    "terms_TFIDF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8638bd75-02e8-4f04-9d5f-242f0d1d8b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TFIDF_inertia = []\n",
    "TFIDF_num_clusters = list(range(1,15))\n",
    "for y in TFIDF_num_clusters:\n",
    "    km = KMeans(n_clusters=y)\n",
    "    km.fit(terms_TFIDF)\n",
    "    TFIDF_inertia.append(km.inertia_)\n",
    "    \n",
    "plt.plot(TFIDF_num_clusters,TFIDF_inertia)\n",
    "plt.scatter(TFIDF_num_clusters,TFIDF_inertia)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9f69d59-a731-4010-afb2-7562c7f5be73",
   "metadata": {},
   "outputs": [
    {
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       "<p>276282 rows × 218995 columns</p>\n",
       "</div>"
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       "[276282 rows x 218995 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF_df = pd.DataFrame(terms_TFIDF.toarray(), columns=TFIDF.get_feature_names_out())\n",
    "TFIDF_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "117b36c8-991b-4106-b0b5-231a412822b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "nmf_model = NMF(10)\n",
    "nmf = nmf_model.fit_transform(terms_TFIDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b974cc9c-814a-469f-a597-b26fdcc7e18f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic: ' NFT Buy/Sell General Discussion '\n",
      "discordhtps, buysel, discusion, everything, live, thank, us, share, subreditmesagecomposetornft, submision\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' Onlyfans '\n",
      "models, click, onlyfans, learn, bevy, directory, sexy, content, new, money\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' Moderator Channel '\n",
      "discords, chanelhtps, fil, contactreditmods, aproved, formhtpsformsgleebzpvoxmnhaxc, alow, removed, doesnt, first\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' NFT Theft '\n",
      "luscious, godbye, preparing, strets, stole, ruin, prepare, lawyer, youi, theft\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' Random '\n",
      "karma, however, contributions, postcoment, engage, automaticaly, combined, award, wait, ask\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' Crypto Discussion '\n",
      "dont, game, im, buy, art, crypto, make, money, know, could\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' NFT Giveaway '\n",
      "discord, fre, giveaway, chanel, adres, verify, drop, server, upvote, send\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' NFT Art '\n",
      "links, submit, discordhtps, hashtags, coment, title, ad, alow, however, removed\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' Opensea '\n",
      "latest, colection, everyonei, herehtpsopenseaiocolectionhelkity, zombie, kity, check, arts, leaving, opensea\n",
      "\n",
      "\n",
      "\n",
      "Topic: ' Metamask '\n",
      "metamask, key, want, ned, xcedaedcfebaefafeb, xabdbafefdaccbbcdfbe, xccdceffefbcfd, xdfcacbeefafbeccdef, xfeffedafcddaeefbbcf, nfthtpsmetamaskzendeskcomhcenusarticlesnfttokensinyourmetamaskwaleti\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TFIDF_topics = show_topics(nmf_model, TFIDF.get_feature_names_out(), 10, ['NFT Buy/Sell General Discussion', 'Onlyfans','Moderator Channel','NFT Theft',\n",
    "                                                                           'Random','Crypto Discussion','NFT Giveaway','NFT Art','Opensea',\n",
    "                                                                         'Metamask'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6668b661-4659-456a-8025-1b18b51423a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276282, 10)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF_df = pd.DataFrame(nmf.round(5),columns = ['NFT Buy/Sell General Discussion', 'Onlyfans','Moderator Channel','NFT Theft',\n",
    "                                                                           'Random','Crypto Discussion','NFT Giveaway','NFT Art','Opensea',\n",
    "                                                                         'Metamask'])\n",
    "TFIDF_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5dc3a227-69f5-4fdf-93d0-fc9d4be0ef39",
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = analysis.reset_index().drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6b61afa7-6a2c-4e07-b494-0a98e4b61af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommender_df = pd.concat([TFIDF_df,analysis],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cc898fb8-1cab-46a2-aea4-8e6410ed75d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommender_df.to_csv('recommender_df')"
   ]
  }
 ],
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