sentiment_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/anweshcr7/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "### Init lists for the analysis\n",
    "\n",
    "HAPPY = set([\n",
    "    ':-)', ':)', ';)', ':o)', ':]', ':3', ':c)', ':>', '=]', '8)', '=)', ':}',\n",
    "    ':^)', ':-D', ':D', '=-D', '=D',\n",
    "    '=-3', '=3', ':-))', \":'-)\", \":')\", ':*', ':^*', '>:P', ':-P', ':P'\n",
    "    , ':-p', ':p', '=p', ':-b', ':b', '>:)', '>;)', '>:-)',\n",
    "    '<3'\n",
    "    ])\n",
    "\n",
    "SAD = set([\n",
    "    ':L', ':-/', '>:/', ':S', '>:[', ':@', ':-(', ':[', ':-||', '=L', ':<',\n",
    "    ':-[', ':-<', '=\\\\', '=/', '>:(', ':(', '>.<', \":'-(\", \":'(\", ':\\\\', ':-c',\n",
    "    ':c', ':{', '>:\\\\', ';('\n",
    "    ])\n",
    "dt = ['not', 'no', 'for', 'and', 'nor', 'but', 'or', 'so',\n",
    "        'while', 'if', 'only', \n",
    "        'until', 'than', \n",
    "         'as', 'after', 'before',\n",
    "        'by', 'now', 'once',\n",
    "        'when', 'because','in',\n",
    "        'why', 'what', 'which', 'who', \n",
    "         'how', 'where','just', 'both', \n",
    "        'with', 'then']\n",
    "\n",
    "conjunctions = ['for', 'and', 'nor', 'but', 'or', 'yet', 'so',\n",
    "        'though', 'although', 'even though', 'while', 'if', 'only if', 'unless',\n",
    "        'until', 'provided that', 'assuming that', 'even if', 'in case', 'than', 'rather than',\n",
    "        'whether', 'as much as', 'whereas', 'after', 'as long as', 'as soon as', 'before',\n",
    "        'by the time', 'now that', 'once', 'since', 'till', 'until',\n",
    "        'when', 'whenever', 'while', 'because', 'since', 'so that', 'in order',\n",
    "        'why', 'that', 'what', 'whatever', 'which', 'whichever', 'who', 'whoever',\n",
    "        'whom', 'whomever', 'whose', 'how', 'as though', 'as if','where', 'wherever',\n",
    "        'also', 'besides', 'furthermore', 'likewise', 'moreover', 'however', 'nevertheless',\n",
    "        'nonetheless', 'still', 'conversely', 'instead', 'otherwise', 'rather', 'accordingly',\n",
    "        'consequently', 'hence', 'meanwhile', 'then', 'therefore', 'thus']\n",
    "\n",
    "NEGATE = {'ain\\'t', 'aren\\'t', 'cannot', 'can\\'t', 'couldn\\'t', 'daren\\'t', 'didn\\'t', 'doesn\\'t',\n",
    " 'ain\\'t', 'aren\\'t', 'cant', 'couldn\\'t', 'daren\\'t', 'didn\\'t', 'doesn\\'t',\n",
    " \"dont\", \"hadnt\", \"hasnt\", \"havent\", \"isnt\", \"mightnt\", \"mustnt\", 'neither',\n",
    " \"don't\", \"hadn't\", \"hasn't\", \"haven't\", \"isn't\", \"mightn't\", \"mustn't\",\n",
    " \"neednt\", \"needn't\", \"never\", \"none\", \"nope\", \"nor\", \"not\", \"nothing\", \"nowhere\",\n",
    " \"oughtnt\", \"shant\", \"shouldnt\", \"uhuh\", \"wasnt\", \"werent\",\n",
    " \"oughtn't\", \"shan't\", \"shouldn't\", \"uh-uh\", \"wasn't\", \"weren't\",\n",
    " \"without\", \"wont\", \"wouldnt\", \"won't\", \"wouldn't\", \"rarely\", \"seldom\", \"despite\",'!'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'vader' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-66b60593b29d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'vader' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_dataset(fp):\n",
    "    '''\n",
    "    Loads the dataset file with label-tweet on each line and parses the dataset.\n",
    "    :param fp: filepath of dataset\n",
    "    :return:\n",
    "        corpus: list of tweet strings of each tweet.\n",
    "        y: list of labels\n",
    "    '''\n",
    "    y = []\n",
    "    corpus = []\n",
    "    with open(fp, 'rt') as data_in:\n",
    "        for line in data_in:\n",
    "            if not line.lower().startswith(\"tweet index\"): # discard first line if it contains metadata\n",
    "                line = line.rstrip() # remove trailing whitespace\n",
    "                label = int(line.split(\"\\t\")[1])\n",
    "                tweet = line.split(\"\\t\")[2]\n",
    "                y.append(label)\n",
    "                corpus.append(tweet)\n",
    "\n",
    "    return corpus, y\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Sweet United Nations video. Just in time for C...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@mrdahl87 We are rumored to have talked to Erv...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hey there! Nice to see you Minnesota/ND Winter...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3 episodes left I'm dying over here</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>\"I can't breathe!\" was chosen as the most nota...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                               tweet  label\n",
       "0  Sweet United Nations video. Just in time for C...      1\n",
       "1  @mrdahl87 We are rumored to have talked to Erv...      1\n",
       "2  Hey there! Nice to see you Minnesota/ND Winter...      1\n",
       "3                3 episodes left I'm dying over here      0\n",
       "4  \"I can't breathe!\" was chosen as the most nota...      1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Train Data\n",
    "\n",
    "data, y_label = parse_dataset('SemEval2018-T3-train-taskA_emoji.txt')\n",
    "data_df = pd.DataFrame(np.array(data).reshape(3834,1), columns = ['tweet'])\n",
    "data_df['label'] = np.array(y_label).reshape(3834,1)\n",
    "data_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 784 entries, 0 to 783\n",
      "Data columns (total 2 columns):\n",
      "tweet    784 non-null object\n",
      "label    784 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 12.4+ KB\n"
     ]
    }
   ],
   "source": [
    "### Test Data\n",
    "\n",
    "# Test the model\n",
    "test_data, test_label = parse_dataset('SemEval2018-T3_gold_test_taskA_emoji.txt')\n",
    "test_df = pd.DataFrame(np.array(test_data).reshape(784,1), columns = ['tweet'])\n",
    "test_df['label'] = np.array(test_label).reshape(784,1)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm \n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10]\n",
    "    gammas = [0.001, 0.01, 0.1, 1]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "    grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n",
    "    grid_search.fit(X, y)\n",
    "    grid_search.best_params_\n",
    "    return grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plotCM(cm):\n",
    "    ax = plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax, fmt=\"d\"); #annot=True to annotate cells\n",
    "\n",
    "    # labels, title and ticks\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the sentiment extraction function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentimentAnalysis(sentencePart, feature):\n",
    "\n",
    "    \n",
    "    sia = SentimentIntensityAnalyzer()\n",
    "    tweet_len = len(sentencePart.split(' '))\n",
    "\n",
    "    pos_word_list=[]\n",
    "    neu_word_list=[]\n",
    "    neg_word_list=[]\n",
    "    highest_pos = float('-inf')\n",
    "    lowest_neg = float('inf')\n",
    "    emoji=[]\n",
    "    negate_flag = False\n",
    "    total_polarity = 0\n",
    "\n",
    "#     threshold = -0.3 -> 0.3\n",
    "    for word in sentencePart.split(' '):\n",
    "        hashtag_flag = False\n",
    "\n",
    "#         do some preprocessing\n",
    "        if(word.startswith('#')):\n",
    "            hashtag_flag = True\n",
    "            word = word.replace('#', '')\n",
    "        \n",
    "        comp_polarity = sia.polarity_scores(word)['compound']\n",
    "        if(hashtag_flag):\n",
    "            comp_polarity = 1.2 * comp_polarity\n",
    "        if(negate_flag):\n",
    "            comp_polarity = -(comp_polarity)\n",
    "        total_polarity += comp_polarity\n",
    "        \n",
    "#         if word = 'not' implies negate flag till punctuation\n",
    "#         if (word in NEGATE):\n",
    "#             negate_flag = True\n",
    "            \n",
    "#         if(word in string.punctuation):\n",
    "#             negate_flag = False\n",
    "            \n",
    "        if (comp_polarity) >= 0.3 or word in HAPPY:\n",
    "            if(comp_polarity > highest_pos):\n",
    "                highest_pos = comp_polarity\n",
    "            pos_word_list.append(word)\n",
    "        \n",
    "        elif (comp_polarity) <= -0.3 or word in NEGATE or word in SAD:\n",
    "            if(comp_polarity < lowest_neg):\n",
    "                lowest_neg = comp_polarity\n",
    "            neg_word_list.append(word)\n",
    "        else:\n",
    "            neu_word_list.append(word)\n",
    "    \n",
    "    if(feature == 'avg'):\n",
    "        return [len(pos_word_list)/tweet_len, len(neu_word_list)/tweet_len, len(neg_word_list)/tweet_len]\n",
    "    if(feature == 'overall'):\n",
    "        return total_polarity/tweet_len\n",
    "    if(feature == 'diff'):\n",
    "        diff = highest_pos - lowest_neg\n",
    "        if(diff == float('-inf') or diff == float('inf')):\n",
    "            return 0\n",
    "        else:\n",
    "            return diff\n",
    "        return highest_pos - lowest_neg\n",
    "    if(feature == 'inversion'):\n",
    "        return len(pos_word_list) and len(neg_word_list)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df['tweet'][3]\n",
    "sentimentAnalysis('3 episodes left Im dying over here', feature = 'avg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def syntactic_feature_extract(og_df):\n",
    "    df = og_df.copy()\n",
    "    df[['positive_avg','neutral_avg', 'negative_avg']] = df['tweet'].apply(lambda x: pd.Series(sentimentAnalysis(x, feature = 'avg')))\n",
    "    df['overall'] = df['tweet'].apply(lambda x: sentimentAnalysis(x, feature = 'overall'))\n",
    "    df['diff'] = df['tweet'].apply(lambda x: sentimentAnalysis(x, feature = 'diff'))\n",
    "    df['inversion'] = df['tweet'].apply(lambda x: sentimentAnalysis(x, feature = 'inversion'))\n",
    "\n",
    "    return df.drop(['tweet'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>positive_avg</th>\n",
       "      <th>neutral_avg</th>\n",
       "      <th>negative_avg</th>\n",
       "      <th>overall</th>\n",
       "      <th>diff</th>\n",
       "      <th>inversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067715</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.009429</td>\n",
       "      <td>0.2263</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046833</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3829</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3830</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.016858</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3831</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014379</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3832</td>\n",
       "      <td>0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017678</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3833</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010419</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3834 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      label  positive_avg  neutral_avg  negative_avg   overall    diff  \\\n",
       "0         1      0.153846     0.846154      0.000000  0.067715  0.0000   \n",
       "1         1      0.041667     0.916667      0.041667  0.009429  0.2263   \n",
       "2         1      0.111111     0.888889      0.000000  0.046833  0.0000   \n",
       "3         0      0.000000     1.000000      0.000000  0.000000  0.0000   \n",
       "4         1      0.000000     0.956522      0.043478  0.000000  0.0000   \n",
       "...     ...           ...          ...           ...       ...     ...   \n",
       "3829      0      0.000000     1.000000      0.000000  0.000000  0.0000   \n",
       "3830      0      0.000000     1.000000      0.000000 -0.016858  0.0000   \n",
       "3831      0      0.000000     1.000000      0.000000  0.014379  0.0000   \n",
       "3832      0      0.055556     0.944444      0.000000  0.017678  0.0000   \n",
       "3833      0      0.000000     1.000000      0.000000 -0.010419  0.0000   \n",
       "\n",
       "      inversion  \n",
       "0             0  \n",
       "1             1  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "3829          0  \n",
       "3830          0  \n",
       "3831          0  \n",
       "3832          0  \n",
       "3833          0  \n",
       "\n",
       "[3834 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syntactic_df = syntactic_feature_extract(data_df)\n",
    "syntactic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = syntactic_df['label']\n",
    "syntactic_df = syntactic_df.drop(['label'], axis=1)\n",
    "\n",
    "test_syntactic_df = syntactic_feature_extract(test_df)\n",
    "y_test = test_syntactic_df['label']\n",
    "test_syntactic_df = test_syntactic_df.drop(['label'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest for feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CROSS VALIDATION\n",
      "Score: [0.55844156 0.55989583 0.55989583 0.56396867 0.56657963 0.56396867\n",
      " 0.58746736 0.57963446 0.54046997 0.53785901]\n",
      "Mean: 0.5618181005787076\n",
      "std: 0.014338066389785341\n"
     ]
    }
   ],
   "source": [
    "# cross-validation on train\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Example of cross-validation\n",
    "rf = RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "score = cross_val_score(rf, syntactic_df, y_train, cv=10, scoring = \"accuracy\")\n",
    "print('CROSS VALIDATION')\n",
    "print(\"Score:\", score)\n",
    "print(\"Mean:\", score.mean())\n",
    "print(\"std:\", score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.62      0.63       473\n",
      "           1       0.45      0.47      0.46       311\n",
      "\n",
      "    accuracy                           0.56       784\n",
      "   macro avg       0.54      0.54      0.54       784\n",
      "weighted avg       0.56      0.56      0.56       784\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf.fit(syntactic_df, y_train)\n",
    "y_pred = rf.predict(test_syntactic_df)\n",
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('positive_avg', 0.10046899499266952)\n",
      "('neutral_avg', 0.1091643741176495)\n",
      "('negative_avg', 0.06194144350726708)\n",
      "('overall', 0.6346505309889992)\n",
      "('diff', 0.08330580845962243)\n",
      "('inversion', 0.010468847933792403)\n"
     ]
    }
   ],
   "source": [
    "feat_labels = list(syntactic_df)\n",
    "for feature in zip(feat_labels, rf.feature_importances_):\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 1}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_param_selection(syntactic_df, y_train, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6224489795918368\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.6448    0.8330    0.7269       473\n",
      "           1     0.5434    0.3023    0.3884       311\n",
      "\n",
      "    accuracy                         0.6224       784\n",
      "   macro avg     0.5941    0.5676    0.5577       784\n",
      "weighted avg     0.6046    0.6224    0.5927       784\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC(C=1, gamma = 0.01, kernel = 'rbf')\n",
    "clf.fit(syntactic_df, y_train)\n",
    "y_pred = clf.predict(test_syntactic_df)\n",
    "print(metrics.accuracy_score(y_test, y_pred))\n",
    "print(metrics.classification_report(y_test, y_pred, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_cm = confusion_matrix(y_test, y_pred)\n",
    "plotCM(svm_cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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