senti_emo_classfication_dystopian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install torch\n",
    "!pip install -qq transformers\n",
    "!pip install NRCLex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys, re\n",
    "import pandas as pd\n",
    "\n",
    "sys.path.insert(0, '/evelynkyl/scifi-sentiment-emotion-classifiction/scripts')\n",
    "\n",
    "# import functions\n",
    "from utils import *\n",
    "from data_processing import *\n",
    "from character_frequency import *\n",
    "from sentiment_analysis_funcs import *\n",
    "from emotion_classification_funcs import *\n",
    "from eval_funcs import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = \"/evelynkyl/scifi-sentiment-emotion-classifiction/data\"\n",
    "dystro_str = read_file_str(datapath)\n",
    "book_names = get_names(datapath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original:  Everything was perfectly swell.  There were no prisons, no slums, no insane asylums, no cripples, no poverty, no wars.  All diseases were conquered. So was old age.  Death, barring accidents, was an adventure for volunteers.  The population of the United States was stabilized at forty-million souls.  One bright morning in the Chicago Lying-in Hospital, a man named Edward K. Wehling, Jr.,  waited for his wife to give birth. He was the only man waiting.  Not many people were born a day any more.  Wehling was fifty-six, a mere stripling in a population whose average age was one hundred and twenty-nine.  X-rays had revealed that his wife was going to have triplets. The children would be his first.  Young Wehling was hunched in his chair, his head in his hand. He was so rumpled, so still and colorless as to be virtually invisible. His camouflage was perfect, since the waiting room had a disorderly and demoralized air, too. Chairs and ashtrays had been moved away from the walls.  The floor w\n",
      " --------------------------------------- \n",
      "Processed:  Everything was perfectly swell.  There were no prisons, no slums, no insane asylums, no cripples, no poverty, no wars.  All diseases were conquered. So was old age.  Death, barring accidents, was an adventure for volunteers.  The population of the United States was stabilized at forty-million souls.  One bright morning in the Chicago Lying-in Hospital, a man named Edward K. Wehling, Jr. ,  waited for his wife to give birth. He was the only man waiting.  Not many people were born a day any more.  Wehling was fifty-six, a mere stripling in a population whose average age was one hundred and twenty-nine.  X-rays had revealed that his wife was going to have triplets. The children would be his first.  Young Wehling was hunched in his chair, his head in his hand. He was so rumpled, so still and colorless as to be virtually invisible. His camouflage was perfect, since the waiting room had a disorderly and demoralized air, too. Chairs and ashtrays had been moved away from the walls.  The floor \n"
     ]
    }
   ],
   "source": [
    "# Clean the texts, including replacing negative contraction ('t) to its full form (not), and normalize punctuations\n",
    "preprocessed_dystro = mass_text_preprocessing(dystro_str)\n",
    "print('Original: ', dystro_str[1][:1000])\n",
    "print(\" --------------------------------------- \")\n",
    "print('Processed: ', preprocessed_dystro[1][:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' If it was good enough for your grandfather, forget it .',\n",
       " 'it is much too good for anyone else!',\n",
       " 'Gramps Ford, his chin resting on his hands, his hands on the crook of his cane, was staring irascibly at the five-foot television screen that dominated the room.',\n",
       " \"On the screen, a news commentator was summarizing the day's happenings.\",\n",
       " 'Every thirty seconds or so, Gramps would jab the floor with his cane-tip and shout, \"Hell, we did that a hundred years ago!\"',\n",
       " \"Emerald and Lou, coming in from the balcony, where they had been seeking that 2185 A. D. rarity--privacy--were obliged to take seats in the back row, behind Lou's father and mother, brother and sister-in-law, son and daughter-in-law, grandson and wife, granddaughter and husband, great-grandson and wife, nephew and wife, grandnephew and wife, great-grandniece and husband, great-grandnephew and wife--and, of course, Gramps, who was in front of everybody.\",\n",
       " 'All save Gramps, who was somewhat withered and bent, seemed, by pre-anti-gerasone standards, to be about the same age--somewhere in their late twenties or early thirties.',\n",
       " 'Gramps looked older because he had already reached 70 when anti-gerasone was invented.',\n",
       " 'He had not aged in the 102 years since.',\n",
       " '\"Meanwhile, \" the commentator was saying, \"Council Bluffs, Iowa, was still threatened by stark tragedy.']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Segement the texts in to list of strings\n",
    "book_sents = mass_sent_tokenizer(preprocessed_dystro)\n",
    "# Remove empty lines\n",
    "book_sents = remove_dot_line(book_sents)\n",
    "book_sents[0][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>all</th>\n",
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       "      <td>it is much too good for anyone else!</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gramps Ford, his chin resting on his hands, hi...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On the screen, a news commentator was summariz...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Every thirty seconds or so, Gramps would jab t...</td>\n",
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       "      <td>It breaks off      abruptly in the middle of a...</td>\n",
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       "      <td>She must have      received warning of the com...</td>\n",
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       "    <tr>\n",
       "      <th>59899</th>\n",
       "      <td>It is to be regretted that she did not live   ...</td>\n",
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       "<p>59900 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                                                     all\n",
       "0       If it was good enough for your grandfather, f...\n",
       "1                   it is much too good for anyone else!\n",
       "2      Gramps Ford, his chin resting on his hands, hi...\n",
       "3      On the screen, a news commentator was summariz...\n",
       "4      Every thirty seconds or so, Gramps would jab t...\n",
       "...                                                  ...\n",
       "59895  The magnitude of the task may be understood wh...\n",
       "59896        This is the end of the Everhard Manuscript.\n",
       "59897  It breaks off      abruptly in the middle of a...\n",
       "59898  She must have      received warning of the com...\n",
       "59899  It is to be regretted that she did not live   ...\n",
       "\n",
       "[59900 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the list of sentences into dataframe for easier data handling\n",
    "df = pd.DataFrame(book_sents)\n",
    "df = df.T # transpose  dataframe\n",
    "df.columns = book_names #rename columns\n",
    "df = df.replace(np.nan, '')\n",
    "# combine all books into one column\n",
    "df_ori = df.melt(value_name='all')['all']\n",
    "df_ori = pd.DataFrame(df_ori)\n",
    "df_ori"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Character Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Find name entities\n",
    "2. Get the top 3 characters by occurrence count\n",
    "2. Get each of their dialogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Edward K. Wehling', 'Young Wehling', 'Hitz', 'Benjamin Hitz', 'Hitz', 'Cannery', 'Mother', 'Lucky Pierre', 'Leora Duncan', 'Duncan', 'Duncan', 'Duncan', 'Leora Duncan', 'Hitz', 'Hitz', 'Zeus', 'Leora Duncan', 'Hitz', 'Duncan', 'Duncan', 'Hitz', 'Leora Duncan', 'Hitz', 'Edward K. Wehling', 'Wehling', 'Hitz', 'Hitz', 'Hitz', 'Hitz', 'Hitz', 'Wehling', 'Hitz', 'Hitz', 'Hitz', 'Hitz', 'Leora Duncan', 'Hitz', 'Hitz', 'Leora Duncan', 'Wehling', 'Hitz', 'Hitz', 'Leora Duncan']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Hitz                 20\n",
       "Leora Duncan          7\n",
       "Duncan                5\n",
       "Wehling               3\n",
       "Edward K. Wehling     2\n",
       "Young Wehling         1\n",
       "Benjamin Hitz         1\n",
       "Cannery               1\n",
       "Mother                1\n",
       "Lucky Pierre          1\n",
       "Zeus                  1\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(list_name_entities(book_sents[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ed</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dr</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>leora</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>painter</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  count\n",
       "0       ed     24\n",
       "2       dr     20\n",
       "3    leora     17\n",
       "1  painter     14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tokenize the whole 2BR02B book into a list of word tokens\n",
    "to_be_or_not_sents = word_tokenizer(list(df[\"2_B_R_0_2_B\"]))\n",
    "\n",
    "\n",
    "character_dict = {\n",
    "                  \"ed\": [\"edward k. wehling jr.\", \"wehling\", \"edward k. wehling\", 'young wehling', \"edward\", \"mr wehling\"],\n",
    "                  \"painter\": [\"the painter\", \"painter\"],\n",
    "                  \"dr\": [\"dr. ritz\", \"dr. benjamin hitz\", \"hitz\", \"benjamin\"],\n",
    "                  \"leora\": [\"leora duncan\", \"leora\", \"duncan\", \"miss duncan\", \"ms duncan\"],\n",
    "                  }\n",
    "\n",
    "# Count frequency of characters\n",
    "character_freq = count_characters(to_be_or_not_tokens, character_dict)\n",
    "\n",
    "# Convert to dataframe for easier comparison\n",
    "name_count_df = pd.DataFrame.from_dict(character_freq, orient='index', columns=[\"count\"])\n",
    "name_count_df = name_count_df.reset_index()\n",
    "name_count_df.columns= [\"name\", \"count\"]\n",
    "name_count_df.sort_values(\"count\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_dialog = flatten_list(get_character_dialogue(to_be_or_not_sents, character_dict, \"ed\"))\n",
    "dr_dialog = flatten_list(get_character_dialogue(to_be_or_not_sents, character_dict, \"dr\"))\n",
    "leora_dialog = flatten_list(get_character_dialogue(to_be_or_not_sents, character_dict, \"leora\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one bright morning in the chicago lying-in hospital, a man named edward k. wehling, jr. ,  waited for his wife to give birth.',\n",
       " 'wehling was fifty-six, a mere stripling in a population whose average age was one hundred and twenty-nine.',\n",
       " 'young wehling was hunched in his chair, his head in his hand.',\n",
       " 'wehling, the waiting father, mumbled something without raising his head.',\n",
       " '\"wehling, \" said the waiting father, sitting up, red-eyed and frowzy.',\n",
       " '\"edward k. wehling, jr. , is the name of the happy father-to-be. \"',\n",
       " '\"oh, mr. wehling, \" said dr. hitz, \"i didn not see you. \"',\n",
       " '\"the invisible man, \" said wehling.',\n",
       " '\"hooray, \" said wehling emptily.',\n",
       " 'said wehling.',\n",
       " 'dr. hitz became rather severe with wehling, towered over him.',\n",
       " 'wehling?\"',\n",
       " '\"i think it\\'s perfectly keen, \" said wehling tautly.',\n",
       " 'wehling?\"',\n",
       " '\"nope, \" said wehling sulkily.',\n",
       " '\"a drupelet, mr. wehling, is one of the little knobs, one of the little pulpy grains of a blackberry, \" said dr. hitz.',\n",
       " 'wehling continued to stare at the same spot on the wall.',\n",
       " '\"i want those kids, \" said wehling quietly.',\n",
       " '\"i don not want my grandfather to die, either, \" said wehling.',\n",
       " '\"this child of yours--whichever one you decide to keep, mr. wehling, \" said dr. hitz.',\n",
       " 'the smile faded as he saw that wehling had just drawn a revolver.',\n",
       " 'wehling shot dr. hitz dead.',\n",
       " \"he took wehling's pistol, really intending to shoot himself.\"]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed_dialog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\"my name\\'s leora duncan. \"',\n",
       " '\"duncan, duncan, duncan, \" he said, scanning the list.',\n",
       " '\"well, \" said leora duncan, \"that\\'s more the disposal people, isn not it?',\n",
       " 'and, while leora duncan was posing for her portrait, into the waitingroom bounded dr. hitz himself.',\n",
       " '\"well, miss duncan!',\n",
       " 'miss duncan!\"',\n",
       " 'said leora duncan.',\n",
       " '\"i wish people wouldn not call it that, \" said leora duncan.',\n",
       " '\"that sounds so much better, \" said leora duncan.',\n",
       " 'and then he shot leora duncan.']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leora_dialog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Whole collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/vocab.json from cache at /home/evelyn/.cache/huggingface/transformers/b522c6365937d6f39045d31ba715daafd39604f04b745f9d3d5cd622ecd74408.bfdcc444ff249bca1a95ca170ec350b442f81804d7df3a95a2252217574121d7\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/merges.txt from cache at /home/evelyn/.cache/huggingface/transformers/a1fbb0cbc048b898b9adb0aa928b6bde50f393786ec91ffe195736820c42b02f.f5b91da9e34259b8f4d88dbc97c740667a0e8430b96314460cdb04e86d4fc435\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/special_tokens_map.json from cache at /home/evelyn/.cache/huggingface/transformers/e7bd01a8669e2d76258ba5ab711ba48da69b2dfc573c7b02566c0e73bd4583f4.0dc5b1041f62041ebbd23b1297f2f573769d5c97d8b7c28180ec86b8f6185aa8\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer_config.json from cache at /home/evelyn/.cache/huggingface/transformers/9f284be68d0cfa5298eea908fc7f51cc7e0b01c7aaf2eba99c70d938b169bfe8.ba4a40df74471cdc82cd580af48bbbcfd25e9095a9d4bb296f711f3af7e2619e\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "  0%|          | 25/7488 [41:22<205:49:32, 99.29s/it]\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/pytorch_model.bin from cache at /home/evelyn/.cache/huggingface/transformers/8fda88182b287ca423f06cae23d4c4701aaba057b65b3a691930de41e6537b3b.f135db3906a2e0c1f328a5920119f906cfb27b985cb0bfe7ef94434bdb31d031\n",
      "All model checkpoint weights were used when initializing RobertaForSequenceClassification.\n",
      "\n",
      "All the weights of RobertaForSequenceClassification were initialized from the model checkpoint at siebert/sentiment-roberta-large-english.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForSequenceClassification for predictions without further training.\n",
      "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "***** Running Prediction *****\n",
      "  Num examples = 59900\n",
      "  Batch size = 8\n",
      "100%|██████████| 7488/7488 [17:45<00:00,  7.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of postive labels vs negative labels:  14433  vs  45467\n",
      "Ratios of postive labels vs negative labels in the whole collection:  (0.24095158597662772, 0.7590484140233723)\n"
     ]
    }
   ],
   "source": [
    "# Perform sentiment analysis using BERT\n",
    "whole_collect = list(df_ori[\"all\"])\n",
    "result_all_data = sentiment_analyzer(whole_collect)\n",
    "\n",
    "# Get the classified sentiment                         \n",
    "positive_num, negative_num = count_polarity(result_all_data)\n",
    "print('Number of postive labels vs negative labels: ', positive_num, \" vs \", negative_num)\n",
    "print('Ratios of postive labels vs negative labels in the whole collection: ', polarity_ratio(result_all_data, positive_num, negative_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for 2BR02B only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of postive labels vs negative labels:  115  vs  144\n",
      "Ratios of postive labels vs negative labels in the whole collection:  (0.444015444015444, 0.555984555984556)\n"
     ]
    }
   ],
   "source": [
    "# Perform sentiment analysis using BERT\n",
    "result_to_be_book = sentiment_analyzer(book_sents[1])\n",
    "\n",
    "# Get the classified sentiment     \n",
    "to_be_positive_num, to_be_negative_num = count_polarity(result_to_be_book)\n",
    "print('Number of postive labels vs negative labels: ', to_be_positive_num, \" vs \", to_be_negative_num)\n",
    "print('Ratios of postive labels vs negative labels in the whole collection: ', polarity_ratio(result_to_be_book, to_be_positive_num, to_be_negative_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for 2BR02B characters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/vocab.json from cache at /home/evelyn/.cache/huggingface/transformers/b522c6365937d6f39045d31ba715daafd39604f04b745f9d3d5cd622ecd74408.bfdcc444ff249bca1a95ca170ec350b442f81804d7df3a95a2252217574121d7\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/merges.txt from cache at /home/evelyn/.cache/huggingface/transformers/a1fbb0cbc048b898b9adb0aa928b6bde50f393786ec91ffe195736820c42b02f.f5b91da9e34259b8f4d88dbc97c740667a0e8430b96314460cdb04e86d4fc435\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/special_tokens_map.json from cache at /home/evelyn/.cache/huggingface/transformers/e7bd01a8669e2d76258ba5ab711ba48da69b2dfc573c7b02566c0e73bd4583f4.0dc5b1041f62041ebbd23b1297f2f573769d5c97d8b7c28180ec86b8f6185aa8\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer_config.json from cache at /home/evelyn/.cache/huggingface/transformers/9f284be68d0cfa5298eea908fc7f51cc7e0b01c7aaf2eba99c70d938b169bfe8.ba4a40df74471cdc82cd580af48bbbcfd25e9095a9d4bb296f711f3af7e2619e\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/pytorch_model.bin from cache at /home/evelyn/.cache/huggingface/transformers/8fda88182b287ca423f06cae23d4c4701aaba057b65b3a691930de41e6537b3b.f135db3906a2e0c1f328a5920119f906cfb27b985cb0bfe7ef94434bdb31d031\n",
      "All model checkpoint weights were used when initializing RobertaForSequenceClassification.\n",
      "\n",
      "All the weights of RobertaForSequenceClassification were initialized from the model checkpoint at siebert/sentiment-roberta-large-english.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForSequenceClassification for predictions without further training.\n",
      "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "***** Running Prediction *****\n",
      "  Num examples = 23\n",
      "  Batch size = 8\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 40.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of postive labels vs negative labels:  7  vs  16\n",
      "Ratios of postive labels vs negative labels in the whole collection:  (0.30434782608695654, 0.6956521739130435)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform sentiment analysis using BERT\n",
    "result_to_be_ed = sentiment_analyzer(ed_dialog)\n",
    "\n",
    "# Get the classified sentiment     \n",
    "ed_positive_num, ed_negative_num = count_polarity(result_to_be_ed)\n",
    "print('Number of postive labels vs negative labels: ', ed_positive_num, \" vs \", ed_negative_num)\n",
    "print('Ratios of postive labels vs negative labels in the whole collection: ', polarity_ratio(result_to_be_ed, ed_positive_num, ed_negative_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>pred</th>\n",
       "      <th>label</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one bright morning in the chicago lying-in hos...</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.996789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wehling was fifty-six, a mere stripling in a p...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.996718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>young wehling was hunched in his chair, his he...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.998107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wehling, the waiting father, mumbled something...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.998563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"wehling, \" said the waiting father, sitting u...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.996208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>\"edward k. wehling, jr. , is the name of the h...</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.998001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>\"oh, mr. wehling, \" said dr. hitz, \"i didn not...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.998021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>\"the invisible man, \" said wehling.</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.989193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\"hooray, \" said wehling emptily.</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.998388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>said wehling.</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.978962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dr. hitz became rather severe with wehling, to...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.997322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>wehling?\"</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.994101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>\"i think it's perfectly keen, \" said wehling t...</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.998485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wehling?\"</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.994101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>\"nope, \" said wehling sulkily.</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.999342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>\"a drupelet, mr. wehling, is one of the little...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.990975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wehling continued to stare at the same spot on...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.999490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>\"i want those kids, \" said wehling quietly.</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.997465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>\"i don not want my grandfather to die, either,...</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.992164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>\"this child of yours--whichever one you decide...</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>0.989559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>the smile faded as he saw that wehling had jus...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.998234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>wehling shot dr. hitz dead.</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.997581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>he took wehling's pistol, really intending to ...</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.997445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text  pred     label  \\\n",
       "0   one bright morning in the chicago lying-in hos...     1  POSITIVE   \n",
       "1   wehling was fifty-six, a mere stripling in a p...     0  NEGATIVE   \n",
       "2   young wehling was hunched in his chair, his he...     0  NEGATIVE   \n",
       "3   wehling, the waiting father, mumbled something...     0  NEGATIVE   \n",
       "4   \"wehling, \" said the waiting father, sitting u...     0  NEGATIVE   \n",
       "5   \"edward k. wehling, jr. , is the name of the h...     1  POSITIVE   \n",
       "6   \"oh, mr. wehling, \" said dr. hitz, \"i didn not...     0  NEGATIVE   \n",
       "7                 \"the invisible man, \" said wehling.     0  NEGATIVE   \n",
       "8                    \"hooray, \" said wehling emptily.     0  NEGATIVE   \n",
       "9                                       said wehling.     1  POSITIVE   \n",
       "10  dr. hitz became rather severe with wehling, to...     0  NEGATIVE   \n",
       "11                                          wehling?\"     0  NEGATIVE   \n",
       "12  \"i think it's perfectly keen, \" said wehling t...     1  POSITIVE   \n",
       "13                                          wehling?\"     0  NEGATIVE   \n",
       "14                     \"nope, \" said wehling sulkily.     0  NEGATIVE   \n",
       "15  \"a drupelet, mr. wehling, is one of the little...     0  NEGATIVE   \n",
       "16  wehling continued to stare at the same spot on...     0  NEGATIVE   \n",
       "17        \"i want those kids, \" said wehling quietly.     1  POSITIVE   \n",
       "18  \"i don not want my grandfather to die, either,...     1  POSITIVE   \n",
       "19  \"this child of yours--whichever one you decide...     1  POSITIVE   \n",
       "20  the smile faded as he saw that wehling had jus...     0  NEGATIVE   \n",
       "21                        wehling shot dr. hitz dead.     0  NEGATIVE   \n",
       "22  he took wehling's pistol, really intending to ...     0  NEGATIVE   \n",
       "\n",
       "       score  \n",
       "0   0.996789  \n",
       "1   0.996718  \n",
       "2   0.998107  \n",
       "3   0.998563  \n",
       "4   0.996208  \n",
       "5   0.998001  \n",
       "6   0.998021  \n",
       "7   0.989193  \n",
       "8   0.998388  \n",
       "9   0.978962  \n",
       "10  0.997322  \n",
       "11  0.994101  \n",
       "12  0.998485  \n",
       "13  0.994101  \n",
       "14  0.999342  \n",
       "15  0.990975  \n",
       "16  0.999490  \n",
       "17  0.997465  \n",
       "18  0.992164  \n",
       "19  0.989559  \n",
       "20  0.998234  \n",
       "21  0.997581  \n",
       "22  0.997445  "
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_to_be_ed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/vocab.json from cache at /home/evelyn/.cache/huggingface/transformers/b522c6365937d6f39045d31ba715daafd39604f04b745f9d3d5cd622ecd74408.bfdcc444ff249bca1a95ca170ec350b442f81804d7df3a95a2252217574121d7\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/merges.txt from cache at /home/evelyn/.cache/huggingface/transformers/a1fbb0cbc048b898b9adb0aa928b6bde50f393786ec91ffe195736820c42b02f.f5b91da9e34259b8f4d88dbc97c740667a0e8430b96314460cdb04e86d4fc435\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/special_tokens_map.json from cache at /home/evelyn/.cache/huggingface/transformers/e7bd01a8669e2d76258ba5ab711ba48da69b2dfc573c7b02566c0e73bd4583f4.0dc5b1041f62041ebbd23b1297f2f573769d5c97d8b7c28180ec86b8f6185aa8\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer_config.json from cache at /home/evelyn/.cache/huggingface/transformers/9f284be68d0cfa5298eea908fc7f51cc7e0b01c7aaf2eba99c70d938b169bfe8.ba4a40df74471cdc82cd580af48bbbcfd25e9095a9d4bb296f711f3af7e2619e\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/pytorch_model.bin from cache at /home/evelyn/.cache/huggingface/transformers/8fda88182b287ca423f06cae23d4c4701aaba057b65b3a691930de41e6537b3b.f135db3906a2e0c1f328a5920119f906cfb27b985cb0bfe7ef94434bdb31d031\n",
      "All model checkpoint weights were used when initializing RobertaForSequenceClassification.\n",
      "\n",
      "All the weights of RobertaForSequenceClassification were initialized from the model checkpoint at siebert/sentiment-roberta-large-english.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForSequenceClassification for predictions without further training.\n",
      "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "***** Running Prediction *****\n",
      "  Num examples = 21\n",
      "  Batch size = 8\n",
      "100%|██████████| 3/3 [00:00<00:00, 28.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of postive labels vs negative labels:  14  vs  7\n",
      "Ratios of postive labels vs negative labels in the whole collection:  (0.6666666666666666, 0.3333333333333333)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform sentiment analysis using BERT\n",
    "result_to_be_dr = sentiment_analyzer(dr_dialog)\n",
    "\n",
    "dr_positive_num, dr_negative_num = count_polarity(result_to_be_dr)\n",
    "print('Number of postive labels vs negative labels: ', dr_positive_num, \" vs \", dr_negative_num)\n",
    "print('Ratios of postive labels vs negative labels in the whole collection: ', polarity_ratio(result_to_be_dr, dr_positive_num, dr_negative_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/vocab.json from cache at /home/evelyn/.cache/huggingface/transformers/b522c6365937d6f39045d31ba715daafd39604f04b745f9d3d5cd622ecd74408.bfdcc444ff249bca1a95ca170ec350b442f81804d7df3a95a2252217574121d7\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/merges.txt from cache at /home/evelyn/.cache/huggingface/transformers/a1fbb0cbc048b898b9adb0aa928b6bde50f393786ec91ffe195736820c42b02f.f5b91da9e34259b8f4d88dbc97c740667a0e8430b96314460cdb04e86d4fc435\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/special_tokens_map.json from cache at /home/evelyn/.cache/huggingface/transformers/e7bd01a8669e2d76258ba5ab711ba48da69b2dfc573c7b02566c0e73bd4583f4.0dc5b1041f62041ebbd23b1297f2f573769d5c97d8b7c28180ec86b8f6185aa8\n",
      "loading file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/tokenizer_config.json from cache at /home/evelyn/.cache/huggingface/transformers/9f284be68d0cfa5298eea908fc7f51cc7e0b01c7aaf2eba99c70d938b169bfe8.ba4a40df74471cdc82cd580af48bbbcfd25e9095a9d4bb296f711f3af7e2619e\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/config.json from cache at /home/evelyn/.cache/huggingface/transformers/228e83e1ade2247aebc5f0725e330fa58dedee3d9eec36c9249f25084a946130.1aece0680a18a95d51d6e1a5f83631412da37b87db65380c52052161354505ba\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"siebert/sentiment-roberta-large-english\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"NEGATIVE\",\n",
      "    \"1\": \"POSITIVE\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"label2id\": {\n",
      "    \"NEGATIVE\": 0,\n",
      "    \"POSITIVE\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.13.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/siebert/sentiment-roberta-large-english/resolve/main/pytorch_model.bin from cache at /home/evelyn/.cache/huggingface/transformers/8fda88182b287ca423f06cae23d4c4701aaba057b65b3a691930de41e6537b3b.f135db3906a2e0c1f328a5920119f906cfb27b985cb0bfe7ef94434bdb31d031\n",
      "All model checkpoint weights were used when initializing RobertaForSequenceClassification.\n",
      "\n",
      "All the weights of RobertaForSequenceClassification were initialized from the model checkpoint at siebert/sentiment-roberta-large-english.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForSequenceClassification for predictions without further training.\n",
      "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "***** Running Prediction *****\n",
      "  Num examples = 10\n",
      "  Batch size = 8\n",
      "100%|██████████| 2/2 [00:00<00:00, 91.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of postive labels vs negative labels:  4  vs  6\n",
      "Ratios of postive labels vs negative labels in the whole collection:  (0.4, 0.6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform sentiment analysis using BERT\n",
    "result_to_be_leora = sentiment_analyzer(leora_dialog)\n",
    "\n",
    "leora_positive_num, leora_negative_num = count_polarity(result_to_be_leora)\n",
    "print('Number of postive labels vs negative labels: ', leora_positive_num, \" vs \", leora_negative_num)\n",
    "print('Ratios of postive labels vs negative labels in the whole collection: ', polarity_ratio(result_to_be_leora, leora_positive_num, leora_negative_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Emotion Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Whole collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {'anticipation': 8911,\n",
       "             'joy': 7125,\n",
       "             'positive': 18018,\n",
       "             'surprise': 4944,\n",
       "             'trust': 10674,\n",
       "             'negative': 15508,\n",
       "             'anger': 6239,\n",
       "             'fear': 8706,\n",
       "             'sadness': 7923,\n",
       "             'disgust': 3803})"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection_sents = flatten_list(book_sents)\n",
    "# Perform classification\n",
    "collection_emo = emo_count(collection_sents)\n",
    "collection_emo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [
    {
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        "plotlyServerURL": "https://plot.ly"
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    "# Visualize the result of emotion classification\n",
    "all_emo_df = dict_to_df(collection_emo)\n",
    "print(visualize_df(all_emo_df, x='Emotion Count', y='Emotion Classification', title=\"Emotional distribution in the entire collection\"))\n",
    "print(visualize_df(multi_2_binary_df(all_emo_df, multiclass_to_binary_count(all_emo_df, 0)), x=\"Count\", y= \"index\", title=\"Binary version of emotional distribution in the entire collection\"))"
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    "### for 2BR02B only"
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   "execution_count": 668,
   "metadata": {},
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      "None\n"
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   ],
   "source": [
    "to_be_emo = emo_count(book_sents[1])\n",
    "to_be_df = dict_to_df(to_be_emo)\n",
    "print(visualize_df(to_be_df, x='Emotion Count', y='Emotion Classification', title=\"Emotional distribution in the 2BR02B book\"))\n",
    "print(visualize_df(multi_2_binary_df(to_be_df, multiclass_to_binary_count(to_be_df, 0)), x=\"Count\", y= \"index\", title=\"Binary version of emotional distribution in the 2BR02B book\"))"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for 2BR02B characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edward's emotion distribution is [('trust', 8), ('positive', 7), ('fear', 6), ('joy', 5), ('negative', 5), ('sadness', 4), ('anticipation', 4), ('surprise', 3), ('anger', 3)]\n",
      "Dr's emotion distribution is [('positive', 10), ('trust', 9), ('joy', 6), ('anticipation', 5), ('sadness', 3), ('negative', 3), ('surprise', 2), ('fear', 2), ('disgust', 1), ('anger', 1)]\n",
      "Leora's emotion distribution is [('negative', 2), ('anger', 1), ('fear', 1), ('sadness', 1), ('surprise', 1)]\n"
     ]
    }
   ],
   "source": [
    "#Character emotion profiling\n",
    "\n",
    "ed_emo = emo_count(ed_dialog)\n",
    "dr_emo = emo_count(dr_dialog)\n",
    "leora_emo = emo_count(leora_dialog)\n",
    "print(\"Edward's emotion distribution is\", sort_dict(ed_emo))\n",
    "print(\"Dr's emotion distribution is\", sort_dict(dr_emo))\n",
    "print(\"Leora's emotion distribution is\", sort_dict(leora_emo))"
   ]
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   "source": [
    "#### Ed"
   ]
  },
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   "execution_count": 875,
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      "  Emotion Classification  Emotion Count\n",
      "2                  trust              8\n",
      "5               positive              7\n",
      "0                   fear              6\n",
      "4                    joy              5\n",
      "7               negative              5\n",
      "1                sadness              4\n",
      "3           anticipation              4\n",
      "6               surprise              3\n",
      "8                  anger              3\n"
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      "None\n"
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   "source": [
    "ed_emo_df = dict_to_df(ed_emo)\n",
    "print(ed_emo_df)\n",
    "print(visualize_df(ed_emo_df, x='Emotion Count', y='Emotion Classification', title=\"Edward Emotional Distribution\")) #27/45 = 0.6\n",
    "print(visualize_df(multi_2_binary_df(ed_emo_df, multiclass_to_binary_count(ed_emo_df, 0)), x=\"Count\", y= \"index\", title=\"Binary version of Edward Emotional Distribution\"))"
   ]
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      "  Emotion Classification  Emotion Count\n",
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     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "dr_emo_df = dict_to_df(dr_emo)\n",
    "print(dr_emo_df)\n",
    "print(visualize_df(dr_emo_df, x='Emotion Count', y='Emotion Classification', title=\"Dr Emotional Distribution\")) #27/45 = 0.6\n",
    "print(visualize_df(multi_2_binary_df(dr_emo_df, multiclass_to_binary_count(dr_emo_df, 0)), x=\"Count\", y= \"index\", title=\"Binary version of Dr Emotional Distribution\"))"
   ]
  },
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   "metadata": {},
   "source": [
    "#### Leora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 880,
   "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
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      "  Emotion Classification  Emotion Count\n",
      "0               negative              2\n",
      "1                  anger              1\n",
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      "4               surprise              1\n"
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      "None\n"
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   "source": [
    "leora_emo_df = dict_to_df(leora_emo)\n",
    "print(leora_emo_df)\n",
    "print(visualize_df(leora_emo_df, x='Emotion Count', y='Emotion Classification', title=\"Leora's Emotional Distribution\")) #27/45 = 0.6\n",
    "print(visualize_df(multi_2_binary_df(leora_emo_df, multiclass_to_binary_count(leora_emo_df, 0)), x=\"Count\", y= \"index\", title=\"Binary version of Leora's Emotional Distribution\"))"
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   "source": [
    "## Evaluation\n",
    "\n",
    "Considering the lack of labeled data and time constraints, I only manually labeled the dataset of the 3 characters in 2BR02B to create an evaluation set. Therefore, automatic evaluation is only performed for the chatacters of the subset of the data. The results from the evaluation set will be a represenation to the entire dataset. The rest of the results is manually evaluated."
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  },
  {
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   "execution_count": null,
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   "source": [
    "# Load eval data\n",
    "\n",
    "eval_bi_leora = pd.read_csv(\"/home/evelyn/Documents/digiphil/final_project/data_label/labeled_leora.csv\", converters={'actual_emo': lambda x: x[:].split(',')})\n",
    "eval_bi_dr = pd.read_csv(\"/home/evelyn/Documents/digiphil/final_project/data_label/labeled_dr.csv\", converters={'actual_emo': lambda x: x[:].split(',')})\n",
    "eval_bi_ed= pd.read_csv(\"/home/evelyn/Documents/digiphil/final_project/data_label/labeled_ed.csv\", converters={'actual_emo': lambda x: x[:].split(',')})"
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  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>actual_label</th>\n",
       "      <th>pred</th>\n",
       "      <th>label</th>\n",
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       "      <th>0</th>\n",
       "      <td>\"my name's leora duncan. \"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>[positive]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"duncan, duncan, duncan, \" he said, scanning t...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>[anticipation]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"well, \" said leora duncan, \"that's more the d...</td>\n",
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       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>[negative]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>and, while leora duncan was posing for her por...</td>\n",
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       "      <td>[positive,  surprise]</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"well, miss duncan!</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>[positive,  surprise]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>miss duncan!\"</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>[positive]</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>said leora duncan.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>[neutral]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>\"i wish people wouldn not call it that, \" said...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>[sadness]</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\"that sounds so much better, \" said leora duncan.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>[positive]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>and then he shot leora duncan.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>[sadness,  anger,  negative]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  actual_label  pred  \\\n",
       "0                         \"my name's leora duncan. \"             1     1   \n",
       "1  \"duncan, duncan, duncan, \" he said, scanning t...             0     0   \n",
       "2  \"well, \" said leora duncan, \"that's more the d...             0     0   \n",
       "3  and, while leora duncan was posing for her por...             1     1   \n",
       "4                                \"well, miss duncan!             1     0   \n",
       "5                                      miss duncan!\"             1     0   \n",
       "6                                 said leora duncan.             1     1   \n",
       "7  \"i wish people wouldn not call it that, \" said...             0     0   \n",
       "8  \"that sounds so much better, \" said leora duncan.             1     1   \n",
       "9                     and then he shot leora duncan.             0     0   \n",
       "\n",
       "      label                    actual_emo  \n",
       "0  POSITIVE                    [positive]  \n",
       "1  NEGATIVE                [anticipation]  \n",
       "2  NEGATIVE                    [negative]  \n",
       "3  POSITIVE         [positive,  surprise]  \n",
       "4  NEGATIVE         [positive,  surprise]  \n",
       "5  NEGATIVE                    [positive]  \n",
       "6  POSITIVE                     [neutral]  \n",
       "7  NEGATIVE                     [sadness]  \n",
       "8  POSITIVE                    [positive]  \n",
       "9  NEGATIVE  [sadness,  anger,  negative]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_bi_leora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leora accuracy score:  0.8\n",
      "Dr accuracy score:  0.8571428571428571\n",
      "Ed accuracy score:  0.8260869565217391\n"
     ]
    }
   ],
   "source": [
    "# accuracy score\n",
    "print(\"Leora accuracy score: \", compute_accuracy(list(eval_bi_leora[\"actual_label\"]), list(result_to_be_leora['pred'])))\n",
    "print(\"Dr accuracy score: \",compute_accuracy(list(eval_bi_dr[\"actual_label\"]), list(result_to_be_dr['pred'])))\n",
    "print(\"Ed accuracy score: \",compute_accuracy(list(eval_bi_ed[\"actual_label\"]), list(result_to_be_ed['pred'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leora F1 score:  0.8\n",
      "Dr F1 score:  0.8444444444444444\n",
      "Ed F1 score:  0.8083333333333333\n"
     ]
    }
   ],
   "source": [
    "# macro F1 score \n",
    "print(\"Leora F1 score: \", f1_score(list(eval_bi_leora[\"actual_label\"]), list(result_to_be_leora['pred']), average='macro'))\n",
    "print(\"Dr F1 score: \", f1_score(list(eval_bi_dr[\"actual_label\"]), list(result_to_be_dr['pred']), average='macro'))\n",
    "print(\"Ed F1 score: \",f1_score(list(eval_bi_ed[\"actual_label\"]), list(result_to_be_ed['pred']), average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leora F1 score:  0.8000000000000002\n",
      "Dr F1 score:  0.8571428571428571\n",
      "Ed F1 score:  0.8260869565217391\n"
     ]
    }
   ],
   "source": [
    "# micro F1 score\n",
    "print(\"Leora F1 score: \", f1_score(list(eval_bi_leora[\"actual_label\"]), list(result_to_be_leora['pred']), average='micro'))\n",
    "print(\"Dr F1 score: \", f1_score(list(eval_bi_dr[\"actual_label\"]), list(result_to_be_dr['pred']), average='micro'))\n",
    "print(\"Ed F1 score: \",f1_score(list(eval_bi_ed[\"actual_label\"]), list(result_to_be_ed['pred']), average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emotion classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "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>sentence</th>\n",
       "      <th>emo_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"my name's leora duncan. \"</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"duncan, duncan, duncan, \" he said, scanning t...</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"well, \" said leora duncan, \"that's more the d...</td>\n",
       "      <td>[negative]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>and, while leora duncan was posing for her por...</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"well, miss duncan!</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>miss duncan!\"</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>said leora duncan.</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>\"i wish people wouldn not call it that, \" said...</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\"that sounds so much better, \" said leora duncan.</td>\n",
       "      <td>[NaN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>and then he shot leora duncan.</td>\n",
       "      <td>[anger, fear, negative, sadness, surprise]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            sentence  \\\n",
       "0                         \"my name's leora duncan. \"   \n",
       "1  \"duncan, duncan, duncan, \" he said, scanning t...   \n",
       "2  \"well, \" said leora duncan, \"that's more the d...   \n",
       "3  and, while leora duncan was posing for her por...   \n",
       "4                                \"well, miss duncan!   \n",
       "5                                      miss duncan!\"   \n",
       "6                                 said leora duncan.   \n",
       "7  \"i wish people wouldn not call it that, \" said...   \n",
       "8  \"that sounds so much better, \" said leora duncan.   \n",
       "9                     and then he shot leora duncan.   \n",
       "\n",
       "                                     emo_pred  \n",
       "0                                       [NaN]  \n",
       "1                                       [NaN]  \n",
       "2                                  [negative]  \n",
       "3                                       [NaN]  \n",
       "4                                       [NaN]  \n",
       "5                                       [NaN]  \n",
       "6                                       [NaN]  \n",
       "7                                       [NaN]  \n",
       "8                                       [NaN]  \n",
       "9  [anger, fear, negative, sadness, surprise]  "
      ]
     },
     "execution_count": 851,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the predicted emotion for each characters, \n",
    "# assign NaN if predicted class is empty\n",
    "\n",
    "leora_emo_pred_df = emo_df_for_eval(leora_dialog)\n",
    "dr_emo_pred_df = emo_df_for_eval(dr_dialog)\n",
    "ed_emo_pred_df = emo_df_for_eval(ed_dialog)\n",
    "leora_emo_pred_df # check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the emotion classification \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean and normalize the dataframes for evaluation\n",
    "for_emo_eval_leora = clean_df_for_emoeval(eval_bi_leora)\n",
    "for_emo_eval_dr = clean_df_for_emoeval(eval_bi_dr)\n",
    "for_emo_eval_ed = clean_df_for_emoeval(eval_bi_ed)\n",
    "\n",
    "normalized_emo_pred_df_leora = normalize_NA_pred(for_emo_eval_leora, leora_emo_pred_df)\n",
    "normalized_emo_pred_df_dr = normalize_NA_pred(for_emo_eval_dr, dr_emo_pred_df)\n",
    "normalized_emo_pred_df_ed = normalize_NA_pred(for_emo_eval_ed, ed_emo_pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leora accuracy score:  0.13333333333333333\n",
      "Dr accuracy score:  0.39682539682539686\n",
      "Ed accuracy score:  0.25362318840579706\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score\n",
    "print(\"Leora accuracy score: \", compute_accruacy_multilabel(for_emo_eval_leora, normalized_emo_pred_df_leora))\n",
    "print(\"Dr accuracy score: \", compute_accruacy_multilabel(for_emo_eval_dr, normalized_emo_pred_df_dr))\n",
    "print(\"Ed accuracy score: \", compute_accruacy_multilabel(for_emo_eval_ed, normalized_emo_pred_df_ed))"
   ]
  }
 ],
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