posTagging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7310a9ee",
   "metadata": {},
   "source": [
    "### Part Of Speech Tagging using stanza"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca9f8ad",
   "metadata": {},
   "source": [
    "<A HREF=\"https://stanfordnlp.github.io/stanza/\">Stanford NLP Stanza</A>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6abc2664",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import stanza\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b001ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trying to avoid memory errors here, may cause problems, don't run unless errors occur\n",
    "#                                     in which case it may be too late anyway.\n",
    "# import os\n",
    "# os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:<512>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c5fb59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1df3ffd1a6ea404ba9a9fd629c85ec74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.5.0.json:   0%|   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 13:42:02 INFO: Downloading default packages for language: en (English) ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08d53a635693415d981507b138a6f342",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading https://huggingface.co/stanfordnlp/stanza-en/resolve/v1.5.0/models/default.zip:   0%|          | 0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 13:42:44 INFO: Finished downloading models and saved to /home/muddy/stanza_resources.\n"
     ]
    }
   ],
   "source": [
    "# Download English model (about 561MB). Downloads from huggingface\n",
    "stanza.download('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9a66726",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 13:42:44 INFO: Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44945b211a744ecdbbc8c150d30bde38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.5.0.json:   0%|   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 13:42:44 INFO: Loading these models for language: en (English):\n",
      "========================\n",
      "| Processor | Package  |\n",
      "------------------------\n",
      "| tokenize  | combined |\n",
      "| pos       | combined |\n",
      "| lemma     | combined |\n",
      "========================\n",
      "\n",
      "2023-04-10 13:42:44 INFO: Using device: cpu\n",
      "2023-04-10 13:42:44 INFO: Loading: tokenize\n",
      "2023-04-10 13:42:44 INFO: Loading: pos\n",
      "2023-04-10 13:42:45 INFO: Loading: lemma\n",
      "2023-04-10 13:42:45 INFO: Done loading processors!\n"
     ]
    }
   ],
   "source": [
    "# Initialize English neural pipeline\n",
    "nlp = stanza.Pipeline('en', processors='tokenize, lemma, pos')\n",
    "\n",
    "# Full list of processors.... only need tokenize and pos for now\n",
    "#============================\n",
    "#| Processor    | Package   |\n",
    "#----------------------------\n",
    "#| tokenize     | combined  |\n",
    "#| pos          | combined  |\n",
    "#| lemma        | combined  |\n",
    "#| depparse     | combined  |\n",
    "#| sentiment    | sstplus   |\n",
    "#| constituency | wsj       |\n",
    "#| ner          | ontonotes |\n",
    "#============================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b92ee4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the two data sets needed\n",
    "\n",
    "facetPlotData = pd.read_csv('./Data/genData/facetPlotData.csv')\n",
    "df = pd.read_csv('./Data/genData/fullEmotionData.csv', encoding='utf_8') # apostrophes aren't being read correctly\n",
    "# Pandas reads date as string, cast to datetime object\n",
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
    "facetPlotData['date'] = pd.to_datetime(facetPlotData['date'], format='%Y-%m-%d')\n",
    "facetPlotData.shape # Should be (3000, 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b93a15ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1,\n",
       " 'text': 'Thank',\n",
       " 'lemma': 'thank',\n",
       " 'upos': 'VERB',\n",
       " 'xpos': 'VBP',\n",
       " 'feats': 'Mood=Ind|Number=Sing|Person=1|Tense=Pres|VerbForm=Fin',\n",
       " 'head': 0,\n",
       " 'deprel': 'root',\n",
       " 'start_char': 0,\n",
       " 'end_char': 5,\n",
       " 'ner': 'O',\n",
       " 'multi_ner': ['O']}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# doc output looks like this.  Everything is labeled, including punctuation!\n",
    "{\n",
    "      \"id\": 1,\n",
    "      \"text\": \"Thank\",\n",
    "      \"lemma\": \"thank\",\n",
    "      \"upos\": \"VERB\",\n",
    "      \"xpos\": \"VBP\",\n",
    "      \"feats\": \"Mood=Ind|Number=Sing|Person=1|Tense=Pres|VerbForm=Fin\",\n",
    "      \"head\": 0,\n",
    "      \"deprel\": \"root\",\n",
    "      \"start_char\": 0,\n",
    "      \"end_char\": 5,\n",
    "      \"ner\": \"O\",\n",
    "      \"multi_ner\": [\n",
    "        \"O\"\n",
    "      ]\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "251d4971",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cumulative_count(items):\n",
    "    '''items: list of objects to count\n",
    "       returns: dictionary with key:item, value: cumulative count of each item'''\n",
    "    counts = defaultdict(int)\n",
    "    cumulative_counts = {}\n",
    "    cumulative_count = 0\n",
    "    \n",
    "    for item in items:\n",
    "        counts[item] += 1\n",
    "        cumulative_count += 1\n",
    "        cumulative_counts[item] = cumulative_count\n",
    "        \n",
    "    return(cumulative_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28e83bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sepecify source: oba, nyt, wsj\n",
    "source = 'wsj'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "685b5cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK Lets put it all together and loop over all speeches and count up the POS\n",
    "# The nlp(text) uses a lot of gpu memory, causes errors sometimes, may need to restart notebook to freshen things up\n",
    "pos_dicts = []\n",
    "for text in df['text_'+source]:\n",
    "    doc = nlp(text) # Run stanza on each speech\n",
    "    mat_of_pos = [[word.pos for word in sentence.words] for sentence in doc.sentences] # matrix of POS for each sentence\n",
    "    # How to flatten a list = [item for sublist in list_of_lists for item in sublist]\n",
    "    list_of_pos = [pos for sentence in mat_of_pos for pos in sentence] # flatten matrix into one list of all pos\n",
    "    pos_counts = cumulative_count(list_of_pos) # get dictionary with key: pos, value: count of that POS in speech\n",
    "    pos_dicts.append(pos_counts) # list of dictionaries, one dict for each speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af16f2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a list of all the unique POS over all the dictionaries, then will make a row in df for each\n",
    "unique_pos = list(set(key for dict in pos_dicts for key in dict.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28d303b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_45825/2872237280.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[source+'_'+key][i] = proportion\n"
     ]
    }
   ],
   "source": [
    "# use unique_pos list to create new columns in df\n",
    "for col in unique_pos:\n",
    "    df[source+'_'+col] = 0\n",
    "    \n",
    "for i in range(len(df)):\n",
    "    sum1 = sum(pos_dicts[i].values())\n",
    "    for key, value in pos_dicts[i].items():\n",
    "        proportion = value/sum1\n",
    "        df[source+'_'+key][i] = proportion\n",
    "df.fillna(value=0); # ; will supress list of whole dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a8981bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>text_oba</th>\n",
       "      <th>text_wsj</th>\n",
       "      <th>text_nyt</th>\n",
       "      <th>emo_oba</th>\n",
       "      <th>emo_nyt</th>\n",
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       "      <th>obafear</th>\n",
       "      <th>obaanger</th>\n",
       "      <th>...</th>\n",
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       "      <th>wsj_CCONJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-06-04-ObamaNomination.txt</td>\n",
       "      <td>Thank you....Thank you. What a -- What a wonde...</td>\n",
       "      <td>Sen. Barack Obama captured enough convention d...</td>\n",
       "      <td>Senator Barack Obama claimed the Democratic pr...</td>\n",
       "      <td>&lt;nrclex.NRCLex object at 0x7f09f2a0eda0&gt;</td>\n",
       "      <td>&lt;nrclex.NRCLex object at 0x7f09e5416260&gt;</td>\n",
       "      <td>&lt;nrclex.NRCLex object at 0x7f09e2782260&gt;</td>\n",
       "      <td>2008-06-04</td>\n",
       "      <td>0.095798</td>\n",
       "      <td>0.055462</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072183</td>\n",
       "      <td>0.071266</td>\n",
       "      <td>0.072023</td>\n",
       "      <td>0.07031</td>\n",
       "      <td>0.072063</td>\n",
       "      <td>0.072621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072422</td>\n",
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       "<p>1 rows × 54 columns</p>\n",
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      "text/plain": [
       "                             file  \\\n",
       "0  2008-06-04-ObamaNomination.txt   \n",
       "\n",
       "                                            text_oba  \\\n",
       "0  Thank you....Thank you. What a -- What a wonde...   \n",
       "\n",
       "                                            text_wsj  \\\n",
       "0  Sen. Barack Obama captured enough convention d...   \n",
       "\n",
       "                                            text_nyt  \\\n",
       "0  Senator Barack Obama claimed the Democratic pr...   \n",
       "\n",
       "                                    emo_oba  \\\n",
       "0  <nrclex.NRCLex object at 0x7f09f2a0eda0>   \n",
       "\n",
       "                                    emo_nyt  \\\n",
       "0  <nrclex.NRCLex object at 0x7f09e5416260>   \n",
       "\n",
       "                                    emo_wsj       date   obafear  obaanger  \\\n",
       "0  <nrclex.NRCLex object at 0x7f09e2782260> 2008-06-04  0.095798  0.055462   \n",
       "\n",
       "   ...  wsj_NOUN  wsj_INTJ  wsj_PROPN   wsj_ADP   wsj_AUX  wsj_SCONJ  \\\n",
       "0  ...  0.072581       0.0   0.072183  0.071266  0.072023    0.07031   \n",
       "\n",
       "   wsj_PART  wsj_PUNCT  wsj_SYM  wsj_CCONJ  \n",
       "0  0.072063   0.072621      0.0   0.072422  \n",
       "\n",
       "[1 rows x 54 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42b560c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the df quickly, before memory errors creep in again.\n",
    "# Now have POS variables with proportion of each POS for each speech\n",
    "#df.to_csv('./Data/genData/fullEmotionPOSData.csv', encoding='utf_8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd07cb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['file',\n",
       " 'text_oba',\n",
       " 'text_wsj',\n",
       " 'text_nyt',\n",
       " 'emo_oba',\n",
       " 'emo_nyt',\n",
       " 'emo_wsj',\n",
       " 'date',\n",
       " 'obafear',\n",
       " 'obaanger',\n",
       " 'obatrust',\n",
       " 'obasurprise',\n",
       " 'obapositive',\n",
       " 'obanegative',\n",
       " 'obasadness',\n",
       " 'obadisgust',\n",
       " 'obajoy',\n",
       " 'obaanticipation',\n",
       " 'nytfear',\n",
       " 'nytanger',\n",
       " 'nyttrust',\n",
       " 'nytsurprise',\n",
       " 'nytpositive',\n",
       " 'nytnegative',\n",
       " 'nytsadness',\n",
       " 'nytdisgust',\n",
       " 'nytjoy',\n",
       " 'nytanticipation',\n",
       " 'wsjfear',\n",
       " 'wsjanger',\n",
       " 'wsjtrust',\n",
       " 'wsjsurprise',\n",
       " 'wsjpositive',\n",
       " 'wsjnegative',\n",
       " 'wsjsadness',\n",
       " 'wsjdisgust',\n",
       " 'wsjjoy',\n",
       " 'wsjanticipation',\n",
       " 'wsj_DET',\n",
       " 'wsj_VERB',\n",
       " 'wsj_ADJ',\n",
       " 'wsj_PRON',\n",
       " 'wsj_ADV',\n",
       " 'wsj_NUM',\n",
       " 'wsj_NOUN',\n",
       " 'wsj_INTJ',\n",
       " 'wsj_PROPN',\n",
       " 'wsj_ADP',\n",
       " 'wsj_AUX',\n",
       " 'wsj_SCONJ',\n",
       " 'wsj_PART',\n",
       " 'wsj_PUNCT',\n",
       " 'wsj_SYM',\n",
       " 'wsj_CCONJ']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30b9a29e",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = df[df.columns[pd.Series(df.columns).str.startswith('oba')]].corr().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a9d9e62",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(corr_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9f6b9983",
   "metadata": {},
   "outputs": [
    {
     "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>obafear</th>\n",
       "      <th>obaanger</th>\n",
       "      <th>obatrust</th>\n",
       "      <th>obasurprise</th>\n",
       "      <th>obapositive</th>\n",
       "      <th>obanegative</th>\n",
       "      <th>obasadness</th>\n",
       "      <th>obadisgust</th>\n",
       "      <th>obajoy</th>\n",
       "      <th>obaanticipation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>obafear</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.679</td>\n",
       "      <td>-0.536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.585</td>\n",
       "      <td>0.566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.502</td>\n",
       "      <td>-0.665</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obaanger</th>\n",
       "      <td>0.679</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.633</td>\n",
       "      <td>0.649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595</td>\n",
       "      <td>-0.582</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obatrust</th>\n",
       "      <td>-0.536</td>\n",
       "      <td>-0.683</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.642</td>\n",
       "      <td>-0.614</td>\n",
       "      <td>-0.590</td>\n",
       "      <td>-0.556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obasurprise</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obapositive</th>\n",
       "      <td>-0.585</td>\n",
       "      <td>-0.633</td>\n",
       "      <td>0.642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.596</td>\n",
       "      <td>-0.636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obanegative</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.649</td>\n",
       "      <td>-0.614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.530</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obasadness</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.596</td>\n",
       "      <td>0.599</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obadisgust</th>\n",
       "      <td>0.502</td>\n",
       "      <td>0.595</td>\n",
       "      <td>-0.556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.636</td>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obajoy</th>\n",
       "      <td>-0.665</td>\n",
       "      <td>-0.582</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obaanticipation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 obafear  obaanger  obatrust  obasurprise  obapositive  \\\n",
       "obafear            1.000     0.679    -0.536          NaN       -0.585   \n",
       "obaanger           0.679     1.000    -0.683          NaN       -0.633   \n",
       "obatrust          -0.536    -0.683     1.000          NaN        0.642   \n",
       "obasurprise          NaN       NaN       NaN          1.0          NaN   \n",
       "obapositive       -0.585    -0.633     0.642          NaN        1.000   \n",
       "obanegative        0.566     0.649    -0.614          NaN       -0.675   \n",
       "obasadness           NaN       NaN    -0.590          NaN       -0.596   \n",
       "obadisgust         0.502     0.595    -0.556          NaN       -0.636   \n",
       "obajoy            -0.665    -0.582       NaN          NaN          NaN   \n",
       "obaanticipation      NaN       NaN       NaN          NaN          NaN   \n",
       "\n",
       "                 obanegative  obasadness  obadisgust  obajoy  obaanticipation  \n",
       "obafear                0.566         NaN       0.502  -0.665              NaN  \n",
       "obaanger               0.649         NaN       0.595  -0.582              NaN  \n",
       "obatrust              -0.614      -0.590      -0.556     NaN              NaN  \n",
       "obasurprise              NaN         NaN         NaN     NaN              NaN  \n",
       "obapositive           -0.675      -0.596      -0.636     NaN              NaN  \n",
       "obanegative            1.000       0.599       0.530  -0.586              NaN  \n",
       "obasadness             0.599       1.000         NaN     NaN              NaN  \n",
       "obadisgust             0.530         NaN       1.000     NaN              NaN  \n",
       "obajoy                -0.586         NaN         NaN   1.000              NaN  \n",
       "obaanticipation          NaN         NaN         NaN     NaN              1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[abs(corr_matrix)>=0.5]\n",
    "# Note: None of the POS proportions correlate with the NRC emotion analysis values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b73b088",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'oba_DET'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'oba_DET'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m source \u001b[38;5;129;01min\u001b[39;00m sources:\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m pos \u001b[38;5;129;01min\u001b[39;00m unique_pos:\n\u001b[0;32m----> 9\u001b[0m             POSFacetPlotData\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;28mlen\u001b[39m(POSFacetPlotData)] \u001b[38;5;241m=\u001b[39m [df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[i], source, pos, \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mpos\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39miloc[i]]\n\u001b[1;32m     11\u001b[0m POSFacetPlotData[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(POSFacetPlotData[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m     12\u001b[0m POSFacetPlotData\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;66;03m# Should be (4800, 4) \u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'oba_DET'"
     ]
    }
   ],
   "source": [
    "# Reshape data for faceted plots\n",
    "# check column names - df[df.columns[pd.Series(df.columns).str.startswith('oba')]]\n",
    "sources = ['oba', 'nyt', 'wsj']\n",
    "POSFacetPlotData  = pd.DataFrame(columns=['date', 'source', 'pos', 'proportion'])\n",
    "\n",
    "for i in range(len(df)):\n",
    "    for source in sources:\n",
    "        for pos in unique_pos:\n",
    "            POSFacetPlotData.loc[len(POSFacetPlotData)] = [df['date'].iloc[i], source, pos, df[source+'_'+pos].iloc[i]]\n",
    "            \n",
    "POSFacetPlotData['date'] = pd.to_datetime(POSFacetPlotData['date'])\n",
    "POSFacetPlotData.shape # Should be (4800, 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "164e81d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save it!\n",
    "#POSFacetPlotData.to_csv('./Data/genData/POSFacetPlotData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b6f65b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Give it 15ish seconds or so\n",
    "# <seaborn.axisgrid.FacetGrid at 0x7efac9f911b0> comes up after about 10 seconds, the plots will follow later\n",
    "times = facetPlotData.date.unique()\n",
    "g = sns.FacetGrid(facetPlotData, col=\"source\", row=\"pos\", height=6, aspect=2.5)\n",
    "g.map(sns.barplot, \"date\", \"proportion\", errorbar=None, order=times)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee9c3043",
   "metadata": {},
   "outputs": [],
   "source": [
    "g.savefig(\"pos_facet_plot.png\", bbox_inches=\"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "018e3722",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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