nat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk import tokenize   \n",
    "import pandas as pd\n",
    "from nltk.util import ngrams\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "from gensim import corpora\n",
    "import gensim\n",
    "import string\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# carrega dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data.tsv\", sep=\"\\t\")['Sobre a pergunta de compra dos produtos indicados pelos meios, por que você compraria algum produto indicado?']\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Função pra criar os ngramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_grams(words, min=1, max=4):\n",
    "    s = []\n",
    "    for n in range(min, max):\n",
    "        for ngram in ngrams(words, n):\n",
    "            s.append(' '.join(str(i) for i in ngram))\n",
    "    return s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tira stopwords, pontuação e lemmatiza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop = set(stopwords.words('portuguese'))\n",
    "exclude = set(string.punctuation) \n",
    "lemma = WordNetLemmatizer()\n",
    "def clean(doc):\n",
    "    stop_free = \" \".join([i for i in doc.lower().split() if i not in stop])\n",
    "    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)\n",
    "    normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\n",
    "    return normalized\n",
    "\n",
    "doc_clean = [word_grams(clean(doc).split(), max=3) for doc in data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cria dicionário word2vec e cria o modelo LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7,\n",
       "  '0.014*\"cara\" + 0.014*\"gosto\" + 0.010*\"vontade\" + 0.010*\"tortas\" + 0.010*\"ver\"'),\n",
       " (8,\n",
       "  '0.047*\"interesse\" + 0.024*\"produto\" + 0.012*\"é\" + 0.008*\"qualidade\" + 0.008*\"vontade\"'),\n",
       " (18,\n",
       "  '0.017*\"facilidade\" + 0.012*\"marca\" + 0.008*\"compraria\" + 0.008*\"porque\" + 0.008*\"anúncio\"')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary = corpora.Dictionary(doc_clean)\n",
    "doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]\n",
    "Lda = gensim.models.ldamodel.LdaModel\n",
    "ldamodel = Lda(doc_term_matrix, num_topics=20, id2word = dictionary, passes=50)\n",
    "\n",
    "# Results \n",
    "ldamodel.print_topics(num_topics=3, num_words=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cria arquivo com os tópicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics = ldamodel.show_topics(20, 5, formatted=False)\n",
    "f_topics = []\n",
    "for topic in topics:\n",
    "    r = [topic[0]]\n",
    "    [r.append(\"{} ({})\".format(i[0], round(i[1], 3))) for i in topic[1]]\n",
    "    f_topics.append(r)\n",
    "pd.DataFrame(f_topics, columns=['Tópico', 'palavra 1', 'palavra 2', 'palavra 3', 'palavra 4', 'palavra 5']).to_csv('topicos.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cria arquivo com as respostas da pesquisa e seus tópicos relevantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "for i, text in enumerate(data):\n",
    "    rrr = [i]\n",
    "    rrr.append(text)\n",
    "    \n",
    "    r = ldamodel.get_document_topics(doc_term_matrix[i], minimum_probability=0.05)\n",
    "    rrr.append(r[0][0])\n",
    "    rrr.append(r[0][1])\n",
    "    if len(r) > 1:\n",
    "        rrr.append(r[1][0])\n",
    "        rrr.append(r[1][1])\n",
    "    else: \n",
    "        rrr.append(-1)\n",
    "        rrr.append(-1)\n",
    "    res.append(rrr)\n",
    "pd.DataFrame(res, columns=['Resposta', 'Texto', 'Tópico 1', 'prob 1', 'Tópico 2', 'prob 2']).to_csv('respostas.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salva modelos pra leitura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpora.MmCorpus.serialize('corpus.mm', doc_term_matrix)\n",
    "ldamodel.save('topic.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = gensim.corpora.MmCorpus('corpus.mm')\n",
    "lda = gensim.models.LdaModel.load('topic.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gera visualização intertópica e de saliência"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Maronato/anaconda/lib/python3.5/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
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       "\n",
       "<div id=\"ldavis_el542041121399565365062891729\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el542041121399565365062891729\", ldavis_el542041121399565365062891729_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el542041121399565365062891729\", ldavis_el542041121399565365062891729_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el542041121399565365062891729\", ldavis_el542041121399565365062891729_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=           Freq  cluster  topics         x         y\n",
       "topic                                               \n",
       "14     8.383789        1       1  0.079013 -0.129962\n",
       "15     8.092895        1       2 -0.142843 -0.065655\n",
       "19     6.427079        1       3  0.118124 -0.074799\n",
       "9      6.359022        1       4 -0.010801 -0.115196\n",
       "2      5.983617        1       5  0.030432 -0.010927\n",
       "8      5.757741        1       6 -0.044469  0.008214\n",
       "3      5.701962        1       7 -0.084348 -0.003802\n",
       "13     5.654242        1       8 -0.033433 -0.000670\n",
       "18     5.454847        1       9  0.106430  0.058240\n",
       "1      4.590203        1      10 -0.037994 -0.038906\n",
       "7      4.578002        1      11 -0.009701  0.126831\n",
       "6      4.476687        1      12 -0.061183  0.001933\n",
       "5      4.411574        1      13  0.019946  0.051348\n",
       "4      4.310427        1      14 -0.019116  0.041858\n",
       "11     4.262390        1      15  0.039108  0.036373\n",
       "16     4.196965        1      16  0.020154  0.047901\n",
       "17     3.735060        1      17 -0.016864  0.028630\n",
       "10     2.658222        1      18  0.003609  0.025194\n",
       "12     2.633763        1      19  0.020171  0.008256\n",
       "0      2.331513        1      20  0.023767  0.005136, topic_info=     Category       Freq                 Term      Total  loglift  logprob\n",
       "term                                                                      \n",
       "1451  Default  17.000000                  sim  17.000000  30.0000  30.0000\n",
       "81    Default  15.000000                preço  15.000000  29.0000  29.0000\n",
       "1590  Default  20.000000                gosto  20.000000  28.0000  28.0000\n",
       "1365  Default  14.000000            qualidade  14.000000  27.0000  27.0000\n",
       "1149  Default   8.000000         experimentar   8.000000  26.0000  26.0000\n",
       "993   Default  20.000000            interesse  20.000000  25.0000  25.0000\n",
       "1482  Default   7.000000                sabor   7.000000  24.0000  24.0000\n",
       "1664  Default   7.000000            aparência   7.000000  23.0000  23.0000\n",
       "399   Default  11.000000           propaganda  11.000000  22.0000  22.0000\n",
       "989   Default   7.000000          necessidade   7.000000  21.0000  21.0000\n",
       "84    Default   5.000000         porque gosto   5.000000  20.0000  20.0000\n",
       "1394  Default  19.000000                    é  19.000000  19.0000  19.0000\n",
       "1679  Default   9.000000          curiosidade   9.000000  18.0000  18.0000\n",
       "594   Default  20.000000            compraria  20.000000  17.0000  17.0000\n",
       "24    Default   9.000000              vontade   9.000000  16.0000  16.0000\n",
       "703   Default   9.000000                 algo   9.000000  15.0000  15.0000\n",
       "1327  Default  40.000000               porque  40.000000  14.0000  14.0000\n",
       "642   Default   3.000000             gostasse   3.000000  13.0000  13.0000\n",
       "794   Default  30.000000              produto  30.000000  12.0000  12.0000\n",
       "615   Default   8.000000               compro   8.000000  11.0000  11.0000\n",
       "1733  Default   8.000000            confiança   8.000000  10.0000  10.0000\n",
       "499   Default   6.000000         interessante   6.000000   9.0000   9.0000\n",
       "976   Default   6.000000             conhecer   6.000000   8.0000   8.0000\n",
       "1641  Default   9.000000                meios   9.000000   7.0000   7.0000\n",
       "1322  Default   6.000000                doces   6.000000   6.0000   6.0000\n",
       "933   Default   3.000000          experiência   3.000000   5.0000   5.0000\n",
       "1692  Default   9.000000                  bom   9.000000   4.0000   4.0000\n",
       "543   Default   4.000000           facilidade   4.000000   3.0000   3.0000\n",
       "1032  Default  14.000000            indicação  14.000000   2.0000   2.0000\n",
       "675   Default   5.000000                 foto   5.000000   1.0000   1.0000\n",
       "...       ...        ...                  ...        ...      ...      ...\n",
       "759   Topic20   0.489301               grande   1.075502   2.9711  -4.9213\n",
       "1415  Topic20   0.489301  ambiente indicações   1.075502   2.9711  -4.9213\n",
       "1166  Topic20   0.489301         eou ambiente   1.075502   2.9711  -4.9213\n",
       "132   Topic20   0.489301        imagem comida   1.075502   2.9711  -4.9213\n",
       "199   Topic20   0.489301               própio   1.075502   2.9711  -4.9213\n",
       "379   Topic20   0.489301       depende desejo   1.075502   2.9711  -4.9213\n",
       "635   Topic20   0.489301     interesse própio   1.075502   2.9711  -4.9213\n",
       "150   Topic20   0.489301     causa divulgação   1.075502   2.9711  -4.9213\n",
       "560   Topic20   0.489301        necessidadrle   1.075502   2.9711  -4.9213\n",
       "1098  Topic20   0.955295                  mim   2.514570   2.7908  -4.2523\n",
       "1441  Topic20   0.489301                causa   1.644908   2.5462  -4.9213\n",
       "1514  Topic20   0.489301             ambiente   1.688956   2.5198  -4.9213\n",
       "912   Topic20   0.955298               desejo   4.071810   2.3088  -4.2523\n",
       "794   Topic20   1.477322              produto  30.176474   0.7418  -3.8163\n",
       "399   Topic20   0.955301           propaganda  11.653190   1.2573  -4.2523\n",
       "1365  Topic20   0.955300            qualidade  14.809812   1.0176  -4.2523\n",
       "1394  Topic20   0.955301                    é  19.511439   0.7419  -4.2523\n",
       "871   Topic20   0.523325                  boa   6.484906   1.2416  -4.8541\n",
       "993   Topic20   0.489301            interesse  20.216071   0.0374  -4.9213\n",
       "1355  Topic20   0.489301               imagem   6.925827   1.1086  -4.9213\n",
       "594   Topic20   0.489301            compraria  20.147317   0.0408  -4.9213\n",
       "1382  Topic20   0.489301                  eou   2.916193   1.9736  -4.9213\n",
       "404   Topic20   0.489301             indicado   4.328711   1.5786  -4.9213\n",
       "1613  Topic20   0.489301              depende   3.679373   1.7411  -4.9213\n",
       "802   Topic20   0.489301    compraria produto   3.055639   1.9269  -4.9213\n",
       "1208  Topic20   0.489301         independente   2.476559   2.1370  -4.9213\n",
       "1308  Topic20   0.489301           indicações   2.369433   2.1812  -4.9213\n",
       "271   Topic20   0.489301            produto é   1.793931   2.4595  -4.9213\n",
       "580   Topic20   0.489301             garantia   3.028496   1.9358  -4.9213\n",
       "625   Topic20   0.489301               comida   2.239663   2.2375  -4.9213\n",
       "\n",
       "[1119 rows x 6 columns], token_table=      Topic      Freq                  Term\n",
       "term                                       \n",
       "1187     17  0.848243                aberto\n",
       "1578     17  0.848243         aberto ttodos\n",
       "245      14  0.834891                 acabo\n",
       "1703     14  0.834891        acabo provando\n",
       "1491     16  0.837768                acesso\n",
       "971      16  0.837768        acesso empresa\n",
       "1385      6  0.236541                 achar\n",
       "1385      8  0.236541                 achar\n",
       "1385     14  0.236541                 achar\n",
       "1385     16  0.236541                 achar\n",
       "1385     17  0.236541                 achar\n",
       "792      14  0.834891             achar irá\n",
       "824       4  0.403225               achasse\n",
       "824      12  0.403225               achasse\n",
       "727      12  0.554292  achasse interessante\n",
       "210      13  0.830388                 achei\n",
       "1486     13  0.830388         achei gostoso\n",
       "1275      2  0.522991                  acho\n",
       "1275     16  0.522991                  acho\n",
       "1621      5  0.405470                acordo\n",
       "1621     10  0.405470                acordo\n",
       "1621     13  0.405470                acordo\n",
       "605      10  0.819324        acordo usuário\n",
       "1457     13  0.559158             acreditar\n",
       "1457     16  0.559158             acreditar\n",
       "1223     13  0.830388  acreditar referência\n",
       "1011      1  0.194663              acredito\n",
       "1011      2  0.194663              acredito\n",
       "1011      5  0.194663              acredito\n",
       "1011      9  0.194663              acredito\n",
       "...     ...       ...                   ...\n",
       "1407     11  0.817691                âmbito\n",
       "974      11  0.817691        âmbito digital\n",
       "1394      1  0.153756                     é\n",
       "1394      2  0.051252                     é\n",
       "1394      3  0.051252                     é\n",
       "1394      4  0.102504                     é\n",
       "1394      5  0.051252                     é\n",
       "1394      6  0.102504                     é\n",
       "1394      7  0.153756                     é\n",
       "1394      8  0.051252                     é\n",
       "1394      9  0.051252                     é\n",
       "1394     13  0.051252                     é\n",
       "1394     16  0.051252                     é\n",
       "1394     17  0.102504                     é\n",
       "1394     19  0.051252                     é\n",
       "1394     20  0.051252                     é\n",
       "413      17  0.848243              é aberto\n",
       "1522      5  0.272126                 é bom\n",
       "1522      6  0.272126                 é bom\n",
       "1522      7  0.272126                 é bom\n",
       "1522     19  0.272126                 é bom\n",
       "380       2  0.753051           é confiança\n",
       "17        5  0.793336               é conta\n",
       "1361      4  0.777968               é fonte\n",
       "1403      4  0.564356               é forma\n",
       "1403     19  0.564356               é forma\n",
       "1421      6  0.797077              é simple\n",
       "131      16  0.837768               é sinal\n",
       "497      10  0.819324                 ótima\n",
       "121      10  0.819324       ótima qualidade\n",
       "\n",
       "[1402 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[15, 16, 20, 10, 3, 9, 4, 14, 19, 2, 8, 7, 6, 5, 12, 17, 18, 11, 13, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(lda, c, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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