cluster.ipynb

{
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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     "name": "stdout",
     "text": [
      "Top terms per cluster:\nCluster 0:\nmedida, empresas, saiba, campanha, espaciais, rotasdocrescimento, sepec, cotoveladas, estados, municípios, essenciais, durante, dia, pandemia, mcticnocombate, \nCluster 1:\ncontra, estados, combate, saúde, coronavírus, brasil, leitos, informa, novo, uti, pacientes, mil, municípios, equipamentos, ação, \nCluster 2:\ncomitê, casa, crise, civil, monitoramento, ações, supervisão, centro, impactos, coletiva, combate, operações, coordenação, ministro, confira, \nCluster 3:\ncombatendo, desafios, segue, pandemia, coronavírus, thread, dias, confira, medidas, enfrentamento, relações, exteriores, governança, sociais, comunicação, \nCluster 4:\npandemia, milhões, leia, ninguémficapratrás, durante, brasileiros, pessoas, brasil, país, ações, meio, enfrentamento, conta, medidas, parte, \n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "from langdetect import detect\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "\n",
    "# parameters for function\n",
    "name = 'brazil_gov'\n",
    "lang = 'portuguese'\n",
    "detect_lang = 'pt'\n",
    "\n",
    "def preprocessing(text):\n",
    "    text = re.sub('((www\\.[\\s]+)|(https?://[^\\s]+))', ' ', text)\n",
    "    text = re.sub('@[A-Za-z0-9_-]+', ' ', text)\n",
    "    text = re.sub('RT', ' ', text)\n",
    "    text = re.sub('#', ' ', text)\n",
    "    text = re.sub('[^\\w\\s]', ' ', text)\n",
    "    text = re.sub('[0-9]', ' ', text) # replace numbers\n",
    "    text = re.sub('\\s\\s+', ' ', text) # replace several spaces\n",
    "    return text\n",
    "\n",
    "original_tweets = []\n",
    "documents = []\n",
    "\n",
    "#f = pd.read_json('data/qcri/' + name +'_geo.json', lines=True)\n",
    "#f = pd.read_json('data/qcri/fiocruz_geo.json', lines=True)\n",
    "#for line in f['text']:\n",
    "#    if detect(line) == detect_lang:\n",
    "#        original_tweets.append(line)\n",
    "#        documents.append(preprocessing(line))\n",
    "    \n",
    "f = pd.read_csv('data/out/' + name + '.csv', 'rb', delimiter = '\\t')\n",
    "for line in f['text']:\n",
    "    if detect(line) == detect_lang:\n",
    "        original_tweets.append(line)\n",
    "        documents.append(preprocessing(line))\n",
    "\n",
    "\n",
    "# source — https://pythonprogramminglanguage.com/kmeans-text-clustering/\n",
    "stopwordsList = set(stopwords.words(lang)) # load list of stopwords of target language\n",
    "stopwordsList.add('covid') # here we add some covid-related words\n",
    "stopwordsList.add('covid19')\n",
    "stopwordsList.add('corona')\n",
    "stopwordsList.add('coronavirus')\n",
    "stopwordsList.add('covid-19')\n",
    "stopwordsList.add('amp')\n",
    "stopwordsList.add('via')\n",
    "\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words=stopwordsList)\n",
    "X = vectorizer.fit_transform(documents)\n",
    "\n",
    "true_k = 5\n",
    "model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)\n",
    "model.fit(X)\n",
    "\n",
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = model.cluster_centers_.argsort()[:, ::-1]\n",
    "terms = vectorizer.get_feature_names()\n",
    "for i in range(true_k):\n",
    "    print(\"Cluster %d:\" % i),\n",
    "    text = ''\n",
    "    for ind in order_centroids[i, :15]:\n",
    "        text = text + str(terms[ind]) + ', '\n",
    "        #print(' %s' % terms[ind])\n",
    "    print(text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# for calculating number of K in clusters\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "sil = []\n",
    "kmax = 10\n",
    "K = range(2, kmax+1)\n",
    "\n",
    "# dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters = k).fit(data_transformed)\n",
    "    labels = kmeans.labels_\n",
    "    sil.append(silhouette_score(data_transformed, labels, metric = 'euclidean'))\n",
    "\n",
    "plt.plot(K, sil, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum_of_squared_distances')\n",
    "plt.title('Silhouette Method For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# source — https://medium.com/analytics-vidhya/how-to-determine-the-optimal-k-for-k-means-708505d204eb\n",
    "\n",
    "# another way for calculating number of K in clusters\n",
    "\n",
    "# distances\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "\n",
    "\n",
    "mas = MaxAbsScaler()\n",
    "mas.fit(X)\n",
    "data_transformed = mas.transform(X)\n",
    "\n",
    "Sum_of_squared_distances = []\n",
    "K = range(1,10)\n",
    "for k in K:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km = km.fit(data_transformed)\n",
    "    Sum_of_squared_distances.append(km.inertia_)\n",
    "\n",
    "plt.plot(K, Sum_of_squared_distances, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum_of_squared_distances')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'[1]': 67, '[4]': 88, '[0]': 61, '[2]': 41, '[3]': 26}"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "freq = {}\n",
    "\n",
    "for d in documents:\n",
    "    Y = vectorizer.transform([d])\n",
    "    prediction = model.predict(Y)\n",
    "    if str(prediction) not in freq:\n",
    "        freq[str(prediction)] = 1\n",
    "    else:\n",
    "        freq[str(prediction)] += 1\n",
    "\n",
    "freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "#COVID19 | Para evitar demissões durante a #pandemia, o @govbr vai pagar os salários dos trabalhadores de pequenas e médias empresas. A medida trouxe alívio para muita gente. 📺||| #coronavirus https://t.co/7M2oVH3N4l\n- - - - - - - - -\n#COVID19| @govbr comprará equipamentos para garantir segurança de policiais e agentes penitenciários, diz @MinEconomia. Além da União, medida visa atender órgãos de segurança pública dos estados e municípios durante a #pandemia➡️ https://t.co/ZJ0VyEb6oI #coronavirus\n\n📷Ag.Brasil https://t.co/kYdTmNSjHA\n- - - - - - - - -\n@TeichNelson @govbr Seguindo o juramento de Hipócrates de salvar vidas\n#FiqueEmCasa #COVID19\n- - - - - - - - -\nBoa noite,\n\nSegue trecho da coletiva de imprensa no @Planalto sobre #Covid19 na qual participei junto ao ministro da saúde @TeichNelson. Assista mais no Youtube do @MCTIC: https://t.co/Lg0S2Pt4HS\n\nCotoveladas Espaciais 🇧🇷🇧🇷🇧🇷\n\n@jairbolsonaro @minsaude @govbr @secomvc #IV https://t.co/BxVUGLVa32\n- - - - - - - - -\nBoa tarde,\n\nEstão abertas as inscrições para o 1º Hackathon sobre #Covid19 nas atividades espaciais organizado pela @Espacial_AEB/@MCTIC.\n\nMaiores informações em: https://t.co/1nOCSUTZUv\n\nCotoveladas Espaciais 🚀🚀🚀\n\n@jairbolsonaro @govbr @secomvc #MCTICnoCOMBATE #IV https://t.co/DmiQeUL7Sl\n- - - - - - - - -\nComo contrapartida ao apoio financeiro liberado para estados e municípios no combate à #covid19, o @govbr e o @MinEconomia propõem que não seja dado aumento para servidores públicos por 18 meses. Medida economizaria quase R$ 100 bi https://t.co/yl1ODLBF4b\n- - - - - - - - -\nHoje às 15h tem mais uma edição do demoday realizado pela #Sepec @mineconomia, por meio do #InovAtivaBr, para identificar soluções que ajudem as empresas a vencerem a crise da #Covid19. Veja ao vivo: https://t.co/ZDuWBlGapr #RotasDoCrescimento @sebrae @abstartups @secomvc @govbr https://t.co/Ii8EiiQl2p\n- - - - - - - - -\nLançamento da Prática Recomendada!\nhttps://t.co/sf25ByVIwC\n#fiqueemcasa #covid19 #usemascara #ventiladorespulmonares #abnt #normatecnica \n@anvisa_oficial @minsaude @MinEconomia @govbr \n@governosp https://t.co/LvBvrl9qJf\n- - - - - - - - -\nO @govbr, por meio do @MinEconomia, criou ferramenta digital gratuita para acesso a relatório de autoavaliação trabalhista, como medida para evitar a transmissão do #coronavírus e prevenir a #Covid19 em ambientes de trabalho. Leia: https://t.co/vRdRkOJ9t1 https://t.co/wsoEnpnDob\n- - - - - - - - -\nOs empresários estão recebendo aquela força para vencerem os desafios da #Covid19. Facilitar o acesso ao crédito em bancos públicos é uma das medidas. Acesse o #VamosVencer criado pela #Sepec @mineconomia e saiba mais: https://t.co/0wD0UvtDW6 #RotasDoCrescimento @govbr @secomvc https://t.co/qVQuCi9VIH\n- - - - - - - - -\nRT @Astro_Pontes: Bom dia,\n\nO @MCTIC está testando uma solução para descontaminação de máscaras N95 em parceria com a @UnB_oficial e o Rotary. \n\nSaiba mais: https://t.co/6tDXryqZYR\n\nCotoveladas Espaciais 👍👍👍\n\n@jairbolsonaro @govbr @secomvc @casacivilbr #MCTICnoCOMBATE #Coronavirus #Covid19 #IV https://t.co/2RSlsSPNS4\n- - - - - - - - -\nRT @EconomiaSepec: As empresas precisam de fôlego no fluxo de caixa para superar os desafios da #Covid19. Entre as medidas do @govbr para ajudar, está o pacote do @bancodobrasil. Acesse o #VamosVencer, criado pela #Sepec @mineconomia e saiba mais: https://t.co/0wD0UvtDW6 #RotasDoCrescimento https://t.co/Skzdpa1x1o\n- - - - - - - - -\nRT @MinCidadania: Estados e municípios que decretaram situação de emergência ou calamidade pública em função da pandemia da #Covid19 já podem realizar inscrições ou atualizações no Cadastro Único do @govbr por telefone ou meio eletrônico. Saiba +: https://t.co/4RpMvVtUUR https://t.co/fifizIzwe7\n- - - - - - - - -\nRT @SSalviano: @IsaVinhosa @Bot_Corona_V @camaradeputados @SenadoFederal @minsaude @govbr A retirada desta matéria ( OSPB - Ciências Política) da grade escolar foi Articulada entre os INIMIGOS que tinha em COMUM o Único OBJETIVO de #DESINSTRUIR a nação para praticar POLITICAGEM. #eleicoes2020 #trasnparencia #coronavirus #covid19 @camaradeputados @SenadoFederal\n- - - - - - - - -\nRT @arnaudmattoso: Não lembro de ver uma queda de ministro tão elegante e tão pra cima quanto esta de @lhmandetta O Ministro está de parabéns pela gestão pela pasta e a gestão desta equipe. Este período já é um dos mais importantes do @minsaude #COVID19 @govbr @CmdoCMP #FiqueEmCasa\n- - - - - - - - -\nRT @avozdobrasil: #COVID19| @govbr reabre prazo para receber doação de computadores, tablets e notebooks. Nova data é o dia 29 de maio, diz @MinEconomia;equipamentos serão utilizados para auxiliar servidores que estejam em teletrabalho https://t.co/bFgvyHOfc7 #coronavirus #homeoffice \n\n📷Pixabay https://t.co/e4l97aYDa7\n- - - - - - - - -\nRT @tvbrasilgov: #COVID19| @govbr totaliza 509 produtos com Imposto de Importação zerado. Nova lista de redução tarifária inclui mais de 80 medicamentos considerados essenciais pelos municípios brasileiros no enfrentamento da #pandemia do #coronavirus|| https://t.co/oEhzx2iho3\n\n📷Pixabay https://t.co/exaPiHpiew\n- - - - - - - - -\nÉ ilógico, ao meu ver, esse sistema d identificar portadores d #COVID19 assintomáticos ou suspeitos e determinarem quarentena domiciliar. \n\nIsso é loucura, vai se espalhar pela casa, cujos moradores tornam-se #vetores.\n\n@minsaude @TeichNelson @govbr @OsmarTerra \n\n#coronavirus\n- - - - - - - - -\n🇧🇷 @govbr orienta acerca da implementação de Hospitais de Campanha | Foram definidos pelo @minsaude regras e financiamento para que os estados e municípios possam implementar as unidades nas cidades, para tratamento de pacientes de #Covid19. Leia: https://t.co/1UX8OVFeLE https://t.co/yyidxoqz1V\n- - - - - - - - -\n🦠 Uma medida provisória do @govbr, já em vigor, reduziu as exigências para liberação de empréstimos a empresas durante a pandemia de #covid19. Saiba mais na @RadioagenciaEBC  https://t.co/LywOAKRWoa\n\n📷 Arquivo/Agência Brasil/© \n@MinEconomia https://t.co/xn1l7I1wAa\n- - - - - - - - -\n"
     ]
    }
   ],
   "source": [
    "tweets = []\n",
    "nums = []\n",
    "for num, d in enumerate(documents):\n",
    "    Y = vectorizer.transform([d])\n",
    "    prediction = model.predict(Y)\n",
    "    if len(tweets) < 20 and prediction == [0]:\n",
    "        if str([d])[2:-2] not in tweets:\n",
    "            tweets.append(str([d])[2:-2])\n",
    "            nums.append(num)\n",
    "\n",
    "final_docs = []\n",
    "for n in nums:\n",
    "    final_docs.append(original_tweets[n])\n",
    "\n",
    "for d in sorted(final_docs):\n",
    "    print(d)\n",
    "    print('- - - - - - - - -')"
   ]
  }
 ],
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