Comment_backToWork.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "\n",
    "from textblob import TextBlob\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.decomposition import PCA\n",
    "import spacy\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "STOPWORDS = set(stopwords.words('english'))\n",
    "from gensim.models import Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('comment_backToWork.csv', encoding=\"ISO-8859-1\")\n",
    "df = df[pd.notnull(df['pick'])]\n",
    "df.rename(columns=lambda x: x.strip(), inplace=True)\n",
    "df = df.drop_duplicates(subset='comment', keep=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
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       "0    Doesn't the targeted approach require massive ...\n",
       "1    I?m at a loss. I made it through 9/11, Super S...\n",
       "2    I'm 76 years old. How incredibly selfish it wo...\n",
       "3    I have zero faith that anyone even remotely at...\n",
       "4    The Katz/ vertical plan has a serious flaw: Ma...\n",
       "Name: comment, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.comment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_state_abbrev = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'American Samoa': 'AS',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'District of Columbia': 'DC',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Guam': 'GU',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Northern Mariana Islands':'MP',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Puerto Rico': 'PR',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virgin Islands': 'VI',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY'\n",
    "}\n",
    "df['state_abbrev'] = df['state'].map(us_state_abbrev).fillna(df['state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
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       "                                             comment       pick state  \\\n",
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
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      "text/plain": [
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       "pick             0\n",
       "state           57\n",
       "state_abbrev    57\n",
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       "CA             176\n",
       "NY             135\n",
       "MA              57\n",
       "PA              36\n",
       "NJ              32\n",
       "              ... \n",
       "Finland          1\n",
       "NB               1\n",
       "Los Angeles      1\n",
       "MT               1\n",
       "IW               1\n",
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       "4  The Katz/ vertical plan has a serious flaw: Ma...  NYT Picks  Utah   \n",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reader Picks    1009\n",
       "NYT Picks         16\n",
       "Name: pick, dtype: int64"
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    "        return 0"
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   "source": [
    "df['comment_len'] = df['comment'].apply(comment_len)\n",
    "nums_comment = df.query('comment_len > 0')['comment_len']\n",
    "\n",
    "fig = ff.create_distplot(hist_data = [nums_comment], group_labels = ['Comment'])\n",
    "fig.update_layout(title_text='Distribution of word count in comment', template=\"plotly_white\")\n",
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   "source": [
    "CA_comment = df.query(\"comment_len > 0 and state_abbrev == 'CA'\")['comment_len']\n",
    "NY_comment = df.query(\"comment_len > 0 and state_abbrev == 'NY'\")['comment_len']\n",
    "MA_comment = df.query(\"comment_len > 0 and state_abbrev == 'MA'\")['comment_len']\n",
    "PA_comment = df.query(\"comment_len > 0 and state_abbrev == 'PA'\")['comment_len']\n",
    "NJ_comment = df.query(\"comment_len > 0 and state_abbrev == 'NJ'\")['comment_len']\n",
    "\n",
    "fig = ff.create_distplot(hist_data=[CA_comment, NY_comment, MA_comment, PA_comment, NJ_comment],\n",
    "                         group_labels=['CA', 'NY', 'MA', 'PA', 'NJ'],\n",
    "                         colors=px.colors.qualitative.Plotly[5:], show_hist=False)\n",
    "\n",
    "fig.update_layout(title_text=\"Comment word count vs. State top 5\", xaxis_title='word count', template=\"plotly_white\")\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "people 728\n",
      "virus 357\n",
      "economy 338\n",
      "need 322\n",
      "work 302\n",
      "health 244\n",
      "don 239\n",
      "testing 228\n",
      "just 222\n",
      "time 218\n",
      "friedman 208\n",
      "like 198\n",
      "weeks 182\n",
      "risk 178\n",
      "medical 171\n",
      "know 167\n",
      "care 164\n",
      "approach 158\n",
      "test 156\n",
      "going 155\n"
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   ],
   "source": [
    "def get_top_n_words(corpus, n=None):\n",
    "    vec = CountVectorizer(stop_words = 'english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "common_words = get_top_n_words(df['comment'], 20)\n",
    "for word, freq in common_words:\n",
    "    print(word, freq)\n",
    "df1 = pd.DataFrame(common_words, columns = ['word' , 'count'])"
   ]
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   "source": [
    "fig = px.bar(df1, x='word', y='count')\n",
    "fig.update_layout(title_text='Comment word count top 20', template=\"plotly_white\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "covid 19 99\n",
      "mr friedman 74\n",
      "health care 71\n",
      "dr katz 58\n",
      "herd immunity 54\n",
      "public health 52\n",
      "stay home 51\n",
      "don know 45\n",
      "south korea 38\n",
      "high risk 35\n",
      "fatality rate 35\n",
      "long term 32\n",
      "social distancing 32\n",
      "people work 29\n",
      "people die 29\n",
      "young people 26\n",
      "mortality rate 26\n",
      "million people 25\n",
      "flatten curve 23\n",
      "death rate 23\n"
     ]
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   ],
   "source": [
    "def get_top_n_bigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(2, 2), stop_words='english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "common_words = get_top_n_bigram(df['comment'], 20)\n",
    "for word, freq in common_words:\n",
    "    print(word, freq)\n",
    "df2 = pd.DataFrame(common_words, columns = ['word' , 'count'])"
   ]
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    "fig = px.bar(df2, x='word', y='count')\n",
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     "text": [
      "test test test 8\n",
      "high risk people 7\n",
      "paid sick leave 7\n",
      "pre existing conditions 7\n",
      "health care professionals 6\n",
      "thank mr friedman 6\n",
      "dr katz approach 6\n",
      "return work school 5\n",
      "health care workers 5\n",
      "emphysema dies lack 5\n",
      "low fatality rate 5\n",
      "high risk groups 5\n",
      "public health officials 5\n",
      "high risk group 5\n",
      "dr katz makes 5\n",
      "forced stay home 4\n",
      "patient chest pain 4\n",
      "patient advanced emphysema 4\n",
      "advanced emphysema dies 4\n",
      "dies lack facility 4\n"
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    "def get_top_n_trigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(3, 3), stop_words='english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "common_words = get_top_n_trigram(df['comment'], 20)\n",
    "for word, freq in common_words:\n",
    "    print(word, freq)\n",
    "df3 = pd.DataFrame(common_words, columns = ['word' , 'count'])"
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   "source": [
    "fig = px.bar(df3, x='word', y='count')\n",
    "fig.update_layout(title_text='Comment trigram count top 20', template=\"plotly_white\")\n",
    "fig.show()"
   ]
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   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
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   "source": [
    "def print_comment(index):\n",
    "    example = df[df.index == index][['comment', 'state_abbrev']].values[0]\n",
    "    if len(example) > 0:\n",
    "        print(example[0])\n",
    "        print('state_abbrev:', example[1])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Doesn't the targeted approach require massive testing? Yes, it does. And we're not there yet. Until we are this advice seems premature.\n",
      "state_abbrev: UT\n"
     ]
    }
   ],
   "source": [
    "print_comment(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Public health professor here. With all due respect to the experts mentioned in the article, the best simulation studies suggest that the mortality rate is higher than 1%. But even those high quality simulations are difficult to interpret because we don't have adequate data on incidence or prevalence. And we don't have adequate data because we don't have adequate testing equipment. And we don't have adequate testing equipment because our health care \"system\" is in tatters, in part because various industries (pharmaceutical, medical device, insurance) have bribed our lawmakers and handcuffed our clinicians (physicians, nurses, etc). As a result of third party intrusions, our clinicians can no longer deliver high quality care and have unprecedented rates of burn out and moral distress. If this unmitigated COVID 19 disaster is not sufficient to move this country toward universal health care, nothing will (at least in my lifetime). Tom, if you want to use your pulpit to jumpstart the economy, start writing about the need for universal health care access and coverage, the abolition of fee for service procedures, and the unfortunate relationships that exist between industry, professional organizations (AMA, AHA) and politicians.\n",
      "state_abbrev: NJ\n"
     ]
    }
   ],
   "source": [
    "print_comment(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First time ever I agree with Ton Friedman. But, even in these deadly serious times, he could not resist delivering his message without taking a poke at the President. And for what? For following the advice of the academia know it all know nothings - Tom's favorite people.\n",
      "state_abbrev: USA\n"
     ]
    }
   ],
   "source": [
    "print_comment(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1025.000000\n",
       "mean        0.076865\n",
       "std         0.153867\n",
       "min        -0.800000\n",
       "25%         0.000000\n",
       "50%         0.073333\n",
       "75%         0.151389\n",
       "max         1.000000\n",
       "Name: comment_polarity, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['comment_polarity'] = df['comment'].map(lambda text: TextBlob(text).sentiment.polarity)\n",
    "df['comment_polarity'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
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   "source": [
    "nums_polarity = df.query('comment_polarity != 1000')['comment_polarity']\n",
    "\n",
    "fig = ff.create_distplot(hist_data = [nums_polarity], group_labels = ['Comment polarity'])\n",
    "fig.update_layout(title_text='Distribution of sentiment polarity in comment', template=\"plotly_white\")\n",
    "fig.show()"
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   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
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   "source": [
    "fig = px.histogram(df, x=\"comment_polarity\")\n",
    "fig.update_layout(title_text='Distribution of sentiment polarity in comment', template=\"plotly_white\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
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   "source": [
    "CA_polarity = df.query(\"comment_polarity != 1000 and state_abbrev == 'CA'\")['comment_polarity']\n",
    "NY_polarity = df.query(\"comment_polarity != 1000 and state_abbrev == 'NY'\")['comment_polarity']\n",
    "MA_polarity = df.query(\"comment_polarity != 1000 and state_abbrev == 'MA'\")['comment_polarity']\n",
    "PA_polarity = df.query(\"comment_polarity != 1000 and state_abbrev == 'PA'\")['comment_polarity']\n",
    "NJ_polarity = df.query(\"comment_polarity != 1000 and state_abbrev == 'NJ'\")['comment_polarity']\n",
    "\n",
    "fig = ff.create_distplot(hist_data=[CA_polarity, NY_polarity, MA_polarity, PA_polarity, NJ_polarity],\n",
    "                         group_labels=['CA', 'NY', 'MA', 'PA', 'NJ'],\n",
    "                         colors=px.colors.qualitative.Plotly[5:], show_hist=False)\n",
    "\n",
    "fig.update_layout(title_text=\"Comment sentiment polarity vs. State top 5\", xaxis_title='sentiment polarity', template=\"plotly_white\")\n",
    "fig.show()"
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   ],
   "source": [
    "df4 = df.loc[(df['state_abbrev'] == 'CA') | (df['state_abbrev'] == 'NY') | (df['state_abbrev'] == 'MA') | (df['state_abbrev'] == 'PA') | (df['state_abbrev'] == 'NJ')]\n",
    "polarity_group = df4.groupby('state_abbrev')['comment_polarity'].mean().reset_index()\n",
    "\n",
    "colors = ['lightslategray',] * 5\n",
    "colors[2] = 'crimson'\n",
    "fig = go.Figure(data=[go.Bar(\n",
    "    x=polarity_group['state_abbrev'].unique(),\n",
    "    y=polarity_group['comment_polarity'],\n",
    "    marker_color=colors \n",
    ")])\n",
    "fig.update_layout(title_text='Lowest average sentment polarity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    '''Make text lowercase, remove text in square brackets, remove punctuation and remove words containing numbers.'''\n",
    "    text = text.lower()\n",
    "    text = re.sub(r'\\[.*?\\]', '', text)\n",
    "    text = re.sub(r'[%s]' % re.escape(string.punctuation), '', text)\n",
    "    text = re.sub(r'\\w*\\d\\w*', '', text)\n",
    "    # Remove a sentence if it is only one word long\n",
    "    if len(text) > 2:\n",
    "        return ' '.join(word for word in text.split() if word not in STOPWORDS)\n",
    "\n",
    "df_clean = pd.DataFrame(df.comment.apply(lambda x: clean_text(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en', disable=['ner', 'parser']) \n",
    "\n",
    "def lemmatizer(text):        \n",
    "    sent = []\n",
    "    doc = nlp(text)\n",
    "    for word in doc:\n",
    "        sent.append(word.lemma_)\n",
    "    return \" \".join(sent)\n",
    "\n",
    "df_clean[\"comment_lemmatize\"] =  df_clean.apply(lambda x: lemmatizer(x['comment']), axis=1)\n",
    "df_clean['comment_lemmatize'] = df_clean['comment_lemmatize'].str.replace('-PRON-', '')"
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  {
   "cell_type": "code",
   "execution_count": 49,
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      "\n",
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       "(145334, 268345)"
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   "source": [
    "sentences = [row.split() for row in df_clean['comment_lemmatize']]\n",
    "\n",
    "w2v_model = Word2Vec(min_count=20,\n",
    "                     window=5,\n",
    "                     size=100,\n",
    "                     workers=4)\n",
    "\n",
    "w2v_model.build_vocab(sentences)\n",
    "w2v_model.train(sentences, total_examples=w2v_model.corpus_count, epochs=w2v_model.iter)"
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       " ('require', 0.9998206496238708),\n",
       " ('small', 0.9998093843460083),\n",
       " ('family', 0.9998053908348083),\n",
       " ('group', 0.9998005628585815),\n",
       " ('live', 0.9997981786727905),\n",
       " ('may', 0.9997856616973877),\n",
       " ('every', 0.9997822642326355),\n",
       " ('many', 0.999780535697937),\n",
       " ('need', 0.9997801184654236)]"
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       " ('still', 0.9998128414154053),\n",
       " ('one', 0.9998122453689575)]"
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   "metadata": {},
   "outputs": [
    {
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       "[('life', 0.999870777130127),\n",
       " ('society', 0.9998369216918945),\n",
       " ('one', 0.9998353719711304),\n",
       " ('month', 0.999813973903656),\n",
       " ('try', 0.9998136162757874),\n",
       " ('make', 0.999812126159668),\n",
       " ('face', 0.9998112320899963),\n",
       " ('continue', 0.9998108148574829),\n",
       " ('everything', 0.9998078346252441),\n",
       " ('another', 0.9998077750205994)]"
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     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "w2v_model.wv.most_similar(positive=['economy'])"
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  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "w2v_model.wv.similarity('health', 'economy')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning:\n",
      "\n",
      "Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "\n"
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   ],
   "source": [
    "model_wv_df = pd.DataFrame(w2v_model[w2v_model.wv.vocab], list(w2v_model.wv.vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords = [\"economy\", \"health\", \"people\", \"virus\",\\\n",
    "            \"need\", \"work\", \"testing\", \"friedman\",\\\n",
    "            \"risk\", \"medical\", \"care\", 'infect', 'president', 'approach', 'week', 'know']\n",
    "words = [word for word in keywords if word in list(w2v_model.wv.vocab)]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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\"title\": {\"text\": \"2D PCA of Word2Vec embeddings\"}, \"xaxis\": {\"anchor\": \"y\", \"autorange\": true, \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Component 1\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Component 2\"}}},\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = model_wv_df.T[words].T\n",
    "pca = PCA(n_components=2)\n",
    "result = pca.fit_transform(X)\n",
    "\n",
    "df = pd.DataFrame(result, columns=[\"Component 1\", \"Component 2\"])\n",
    "df[\"Word\"] = keywords\n",
    "df[\"Distance\"] = np.sqrt(df[\"Component 1\"]**2 + df[\"Component 2\"]**2)\n",
    "fig = px.scatter(df, x=\"Component 1\", y=\"Component 2\", text=\"Word\", color=\"Distance\", color_continuous_scale=\"agsunset\",size=\"Distance\")\n",
    "fig.update_traces(textposition='top center')\n",
    "fig.layout.xaxis.autorange = True\n",
    "fig.data[0].marker.line.width = 1\n",
    "fig.data[0].marker.line.color = 'rgb(0, 0, 0)'\n",
    "fig.update_layout(height=800, title_text=\"2D PCA of Word2Vec embeddings\", template=\"plotly_white\", paper_bgcolor=\"#f0f0f0\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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