tutorials/explainers/LIME/lime_text.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f6affbc",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<img width=\"150\" alt=\"Logo_ER10\" src=\"https://user-images.githubusercontent.com/3244249/151994514-b584b984-a148-4ade-80ee-0f88b0aefa45.png\">\n",
    "\n",
    "### Interpreting a movie review sentiment model with LIME\n",
    "This notebook demonstrates the use of DIANNA with the LIME method on the [Stanford Sentiment Treebank dataset](https://nlp.stanford.edu/sentiment/index.html) which contains one-sentence movie reviews. See also [their paper](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf). A pre-trained neural network classifier is used, which identifies whether a movie review is positive or negative.\n",
    "\n",
    "LIME (Local Interpretable Model-agnostic Explanations) is an explainable-AI method that aims to create an interpretable model that locally represents the classifier. For more details see the [LIME paper](https://arxiv.org/abs/1602.04938)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224e1862",
   "metadata": {},
   "source": [
    "#### Colab Setup"
   ]
  },
  {
   "cell_type": "code",
   "id": "df22c5aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:47:55.852747Z",
     "start_time": "2024-09-24T07:47:55.848544Z"
    }
   },
   "source": [
    "running_in_colab = 'google.colab' in str(get_ipython())\n",
    "if running_in_colab:\n",
    "    # install dianna\n",
    "    !python3 -m pip install dianna[notebooks]"
   ],
   "outputs": [],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "id": "091947e1",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### 1. Imports and paths"
   ]
  },
  {
   "cell_type": "code",
   "id": "bf888085",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:47:56.742624Z",
     "start_time": "2024-09-24T07:47:56.737825Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import spacy\n",
    "from scipy.special import expit as sigmoid\n",
    "\n",
    "import dianna\n",
    "from dianna import utils\n",
    "from dianna import visualization\n",
    "from dianna.utils.downloader import download\n",
    "from dianna.utils.tokenizers import SpacyTokenizer\n",
    "import pandas as pd"
   ],
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "4263ce5c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:48:00.464556Z",
     "start_time": "2024-09-24T07:48:00.444113Z"
    }
   },
   "source": [
    "model_path = download('movie_review_model.onnx', 'model')\n",
    "word_vector_path = download('movie_reviews_word_vectors.txt', 'data')\n",
    "labels = (\"negative\", \"positive\")"
   ],
   "outputs": [],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "id": "86fd772b",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### 2. Loading the model\n",
    "\n",
    "The classifier is stored in ONNX format. It accepts numerical tokens as input, and outputs a score between 0 and 1, where 0 means the review is negative and 1 that it is positive.  \n",
    "Here we define a class to run the model, which accepts a sentence (i.e. string) as input instead and returns two classes: negative and positive."
   ]
  },
  {
   "cell_type": "code",
   "id": "f09c7fc3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:48:04.770153Z",
     "start_time": "2024-09-24T07:48:01.340062Z"
    }
   },
   "source": [
    "# ensure the tokenizer for english is available\n",
    "spacy.cli.download('en_core_web_sm')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[38;5;2m✔ Download and installation successful\u001B[0m\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n",
      "\u001B[38;5;3m⚠ Restart to reload dependencies\u001B[0m\n",
      "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
      "order to load all the package's dependencies. You can do this by selecting the\n",
      "'Restart kernel' or 'Restart runtime' option.\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "id": "02e6d4b1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:48:08.759550Z",
     "start_time": "2024-09-24T07:48:08.752171Z"
    }
   },
   "source": [
    "class MovieReviewsModelRunner:\n",
    "    def __init__(self, model, word_vectors, max_filter_size):\n",
    "        self.run_model = utils.get_function(model)\n",
    "        self.keys = list(pd.read_csv(word_vector_path, header=None, delimiter=' ')[0])\n",
    "        self.max_filter_size = max_filter_size\n",
    "        \n",
    "        self.tokenizer = SpacyTokenizer(name='en_core_web_sm')\n",
    "\n",
    "    def __call__(self, sentences):\n",
    "        # ensure the input has a batch axis\n",
    "        if isinstance(sentences, str):\n",
    "            sentences = [sentences]\n",
    "\n",
    "        output = []\n",
    "        for sentence in sentences:\n",
    "            # tokenize and pad to minimum length\n",
    "            tokens = self.tokenizer.tokenize(sentence.lower())\n",
    "            if len(tokens) < self.max_filter_size:\n",
    "                tokens += ['<pad>'] * (self.max_filter_size - len(tokens))\n",
    "            \n",
    "            # numericalize the tokens\n",
    "            tokens_numerical = [self.keys.index(token) if token in self.keys else self.keys.index('<unk>')\n",
    "                                for token in tokens]\n",
    "\n",
    "            # run the model, applying a sigmoid because the model outputs logits, remove any remaining batch axis\n",
    "            pred = float(sigmoid(self.run_model([tokens_numerical])))\n",
    "            output.append(pred)\n",
    "\n",
    "        # output two classes\n",
    "        positivity = np.array(output)\n",
    "        negativity = 1 - positivity\n",
    "        return np.transpose([negativity, positivity])       "
   ],
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "id": "214007db",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:48:09.989126Z",
     "start_time": "2024-09-24T07:48:09.389420Z"
    }
   },
   "source": [
    "# define model runner. max_filter_size is a property of the model\n",
    "model_runner = MovieReviewsModelRunner(model_path, word_vector_path, max_filter_size=5)"
   ],
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "id": "7939aef9",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### 3. Applying LIME with DIANNA\n",
    "The simplest way to run DIANNA on text data is with `dianna.explain_text`. The arguments are:\n",
    "* The function that runs the model (a path to a model in ONNX format is also accepted)\n",
    "* The text we want to explain\n",
    "* The name of the explainable-AI method we want to use, here LIME\n",
    "* The numerical index of the class we want an explanation for\n",
    "\n",
    "`dianna.explain_text` returns a list of tuples. Each tuple contains a word, its location in the input text, and its importance for the selected output class"
   ]
  },
  {
   "cell_type": "code",
   "id": "083ff6b1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:48:16.138275Z",
     "start_time": "2024-09-24T07:48:16.134837Z"
    }
   },
   "source": [
    "review = \"A delectable and intriguing thriller filled with surprises.\""
   ],
   "outputs": [],
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "id": "1b2b2ab2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:49:41.640025Z",
     "start_time": "2024-09-24T07:48:17.677391Z"
    }
   },
   "source": [
    "# We're getting the explanation for the 'positive' class only,\n",
    "# but dianna supports explaining for multiple labels in one go.\n",
    "# It therefore always outputs a list of saliency maps. We want\n",
    "# the first and only saliency map from this list here.\n",
    "explanation_relevance = dianna.explain_text(model_runner, review, model_runner.tokenizer,\n",
    "                                            'LIME', labels=[labels.index('positive')])[0]\n",
    "explanation_relevance"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ChristiaanMeijer\\AppData\\Local\\Temp\\ipykernel_24288\\920258139.py:26: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  pred = float(sigmoid(self.run_model([tokens_numerical])))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('A', 0, 0.015866258534045923),\n",
       " ('delectable', 1, 0.056779367404829105),\n",
       " ('and', 2, 0.02010577523931426),\n",
       " ('intriguing', 3, 0.15199938297130808),\n",
       " ('thriller', 4, 0.06191921460301456),\n",
       " ('filled', 5, -0.009348110187351329),\n",
       " ('with', 6, 0.012401232573987918),\n",
       " ('surprises', 7, 0.0033519606402072225),\n",
       " ('.', 8, 0.005006942933185387)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "id": "a3ef466a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### 4. Visualization\n",
    "DIANNA includes a visualization package, capable of highlighting the relevance of each word in the text for a chosen class. The visualization is in HTML format.\n",
    "Words in favour of the selected class are highlighted in red, while words against the selected class - in blue."
   ]
  },
  {
   "cell_type": "code",
   "id": "1bbba305",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-09-24T07:49:47.558460Z",
     "start_time": "2024-09-24T07:49:47.448027Z"
    }
   },
   "source": [
    "fig, _ = visualization.highlight_text(explanation_relevance, model_runner.tokenizer.tokenize(review))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x100 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "markdown",
   "id": "1f460858",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The most important word for this review being classified as positive is \"intriguing\", followed by \"delectable\" and \"thriller\".\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9818c180-322d-4a52-8170-7f7e0fe70e31",
   "metadata": {},
   "source": [
    "#### Trying more text explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d997d9c0-6277-4778-b9a4-3248a8c8ea93",
   "metadata": {},
   "source": [
    "And what are the words' relevances to a '**negative**' classification decision?"
   ]
  },
  {
   "cell_type": "code",
   "id": "24ffddee-6e39-4a0d-876d-2622c96a5195",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:51:12.987181Z",
     "start_time": "2024-09-24T07:49:51.811355Z"
    }
   },
   "source": [
    "explanation_relevance = dianna.explain_text(model_runner, review, model_runner.tokenizer,\n",
    "                                            'LIME', labels=[labels.index('negative')])[0]\n",
    "explanation_relevance"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ChristiaanMeijer\\AppData\\Local\\Temp\\ipykernel_24288\\920258139.py:26: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  pred = float(sigmoid(self.run_model([tokens_numerical])))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('A', 0, -0.019514978089691937),\n",
       " ('delectable', 1, -0.05962810304456748),\n",
       " ('and', 2, -0.01683303947825472),\n",
       " ('intriguing', 3, -0.15045311584825755),\n",
       " ('thriller', 4, -0.0651238678922685),\n",
       " ('filled', 5, 0.011041035541857452),\n",
       " ('with', 6, -0.016607320707398725),\n",
       " ('surprises', 7, -0.006697423484235601),\n",
       " ('.', 8, -0.0020763731322912186)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "id": "9043f3e5-79da-4dbf-8190-17b6ecdf2f1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:51:13.181929Z",
     "start_time": "2024-09-24T07:51:13.076714Z"
    }
   },
   "source": [
    "fig, _ = visualization.highlight_text(explanation_relevance, model_runner.tokenizer.tokenize(review))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x100 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "markdown",
   "id": "d27ae57f-f2ff-4089-b804-06e58ce5af67",
   "metadata": {},
   "source": [
    "And what about a harder to classify review with mixed words, but still a clear overall sentiment?"
   ]
  },
  {
   "cell_type": "code",
   "id": "e3846fef-abfd-4e49-b4de-bf437205dd18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:51:13.219506Z",
     "start_time": "2024-09-24T07:51:13.216181Z"
    }
   },
   "source": [
    "mixed_review = 'The movie started great but the ending is boring and unoriginal!'"
   ],
   "outputs": [],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "id": "919f2f12-7c04-4fa2-9f83-2043a450dc07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:52:34.792349Z",
     "start_time": "2024-09-24T07:51:13.253989Z"
    }
   },
   "source": [
    "explanation_relevance = dianna.explain_text(model_runner, mixed_review, model_runner.tokenizer,\n",
    "                                            'LIME', labels=[labels.index('negative')])[0]\n",
    "explanation_relevance"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ChristiaanMeijer\\AppData\\Local\\Temp\\ipykernel_24288\\920258139.py:26: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  pred = float(sigmoid(self.run_model([tokens_numerical])))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('movie', 1, -0.03673063275292124),\n",
       " ('great', 3, -0.2260445326024729),\n",
       " ('but', 4, 0.009804884666921327),\n",
       " ('the', 5, -0.023609172345536004),\n",
       " ('ending', 6, 0.10596165300878727),\n",
       " ('is', 7, 0.0328580853298903),\n",
       " ('boring', 8, 0.4543361377485931),\n",
       " ('and', 9, 0.009474746608939101),\n",
       " ('unoriginal', 10, 0.05610043478029175),\n",
       " ('!', 11, 0.10317066988731821)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "id": "86247d24-6c7e-46d5-89c0-2f51c7905988",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T07:52:34.904089Z",
     "start_time": "2024-09-24T07:52:34.800056Z"
    }
   },
   "source": [
    "fig, _ = visualization.highlight_text(explanation_relevance, model_runner.tokenizer.tokenize(mixed_review))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x100 with 2 Axes>"
      ],
      "image/png": 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VP3/+vAAggoKClMrV2XYFvL29haGhoYiNjVWU3bx5U+jo6AgAIj1diDNnSn5169ZfGBoai/37HyrKdu2KFDo6ugKAogyAkEqlYvv2G0rz9+8/QdSpU1ccPpygVO7uPlyYmJiJ0NBn4swZIU6dyhV//JGtVCckJFnUqmUt+vUbryg7dOhJ/nvOX1F29YpciNzcEl/+c+YIAGJA//5K5X6TJwsA4uqlSyI6KiqvrzdsKDY/AOE/Z47i/2VLlggAIjoqSlEme54rsrJEia9Fi5YJAOL27WilcgBCX19f3LgRpSg7f/6qACBWrFipKPPxmSDq1q0rHj5MUJp/yJDhwszMTKQlpAnx4EGprwnDh4u61tYi4do1pfLh77wjzExNxbPISBG6a1fe50eTJiL7n38Udb4NDMz7/Dh+XIgHD0ROdLSwqlNHtG/VSqneuvy+cevSRVEWHR6e17dff60oGztkiAAg5s2YoRRLh9athVPbtor/96xbJwCIbwICFGWye/dEr65dBQCxf8uW0nfiZ89E0Nq1edvr1i2x9+efBQBx/tSp4nVjYoRYurTEl7+7e977qGVLpXI/F5e899Enn4gr06YJAOL9zp2V6szo0UMAEL/7+irK7CwsBABxZMKEYuuys7AQY52cFP8HDR0qAAj3Jk2EfMkSRfkn3bsLHalUpAQGCrF0qYibM0foSqXCu1UrpeUFvPWWAJC3zE2bSt1fnqWnFysLP31aABBbCs0btGFDXky9ewv58+eK8k8+/ljo6OiIlMREIXJzRfyjR0JfX194eXoq1fvfrFl5Mb33nhDZ2UKkpJT4GjtihLBr0KBYuf/nn+d9F+b/X7A/RV2+rCi7mh/7yqVLFWXeXl5CX19f3L1yRVH27+3bombNmqKHq6uiLGj1agFAdHNxEbmJiUrrLpgWffWqECkpIu7vv4Wurq7w9vJSqhdQ0M4RIxRloQcOCAAi9MABRZlb/nt6y5o1irLs+HhhY20tBg0YoCjLTUwU2fHxSutIjokR1lZWYvzo0UrlAIT/558LkZhY5udD0Ndf57UnPLzMuqLIdxsRvbw0ehnVuHHjlM4EFPwy/s8//wAArly5gsjISIwcORKJiYlISEhAQkICnj59it69e+PkyZNqjxvw8fnvF1EdHR288UZHCCEwduwERbm5uTmaNGmG6Oi89YeEHIK1tQ2GDh2hqKOnpwc/v4+QkZGBU6f+AAD07NkLderUwZ49/53+T05Oxu+/H8OgQcNKjCkpKQlhYb/j3XeHIj09XdG+xMREuLt7ICoqEg8fPiyzbebm5oiIiCj1MhlVdHR0FP0vl8uRlJSE3NxcdOzYEZcuXSpWf9CgQYpfusui7raTyWQICQmBt7c3GjZsqJi/RYsW8PDwKHM9MpkM588fR48e3rC0rKcot7VtDBeXvsXqt2/vBgeHlor/hRAIC9uDrl37QwiBlJQExcvZ2QMZGam4c+eSor/09P7rr7S0JMhkuWjevKOizouaUugyBgD4cMoUAMChw4crZfkV1auXOxwdHRX/t2nTFqampop9RQiBffv2wNMzrx8LtndCQgLeessDqampuH3ndqnrEEJgz+HD6O/unreMpCTFy8PNDalpabh0/bqi/rihQ5U/P5ydAQD/3LsHALjw11+IT0jApDFjlOr5DB0KM1NTtds+acwYpf+7d+6MfwpdlnIkLAx6enqYOGqUokwqlWJKkTME6ioYnH/w8GE8L3TpU3lMyT+7WODDrl0BAIdu38ah23nbYXr+522BT93cAAC/3VbeTg61asGjWTO11+3r7Kx0y+ruDg6QyeWIzb/L2YmoKOTK5fBzcVEZozqMjIwUfz9//hyJiYlo3LgxzM3NVX52+U6cqBxT9+6QyWSIzd+Ox0+cQE5ODj6cMkWp3rSPP1Y7pvJw79kTjg4Oiv/btm4NU1NT/JN/GZhMJsPR0FB4e3mhUf5lrwBQ18YGIwcPxumzZ5GWlqa0zInvvVfm+IwTf/yB3Nxc+BU6QwgAH5bjEj0TExOMHvbf95q+vj46v/GGInZAxXdLcjJyZTJ07NABl65eVXtdRPR60OhlVIUPLoH/LvNITk4GAERGRgIAxpbypZ2RkQEDg7IPHBo0UF6XmZkZDA0NFZeYFC5PSkoEANy7F4vGjZtAKlXOuZo1a6GYDgC6urp4551B2LVrB7Kzs2FgYID9+4Px/PlzDB5ccrJx924UhBCYN28O5s2bo7LOkyfxqF+/fqltW7p0KcaOHYsGDRrAyckJnp6eeO+999CoUaNS5wOAzZs3Y/ny5bh9+7bSgY1DoS/C0spKos62S01NRXZ2NjIzM9GkSZNi05s1a4ZDhw6Vup7k5HhkZ2fC1rZxsWn16xcvq1dPuQ3JyU+Qnp6C/fvXYf/+dSWuo8ChQ5vx00/LERt7G7m5//VX0eVWVNF+cHR0hFQqfeHnL7yoovsPAJibWyj21SdPniAlJQUbNqzDhg0l9WMSYFdP5TQAeJKYiJTUVKzbvh3rtm9XWSc+IQEWZmYAgIZF9ouC8uTUVABA7IMHAIAmRd63enp6aNSweHtUMTQwgGX+ZWKF11OwDgCIffgQda2sYFzoABgAGhc6SCwPt+7dMcjbG4FffokVq1ahZ/fu8O7fHyOHDYO69xRqUuRzzbF2bUglEsQkJQEApBIJGhepY1OzJsyNjBCbv00LOORf1qOuhkXuZGaR3y/J+Zf2FCy/6PprGRsr6pYlMzMTixYvRtDmzXj48CGEEIppqUUOwgGgYYMGyjHlx1jw/i1IOoruf5aWlqVeflhRDW1ti5VZmJkhOT8he5KQgGfPnqFZ4+KfYS2aNoVcLsf9hw/RqlDS7GBnV+Z6Y/NvGdu4yD5Ry8JC0Sdlsa1Xr9jzjyzMzfFXkfEtm3fswPJVq3A7MlL5u0WNOIno9aLRZKOkX2EKvjgKzlosW7asxNuqGhsbochYTbXXVdb6y2PIkOHYsGEtQkIOY8AAbwQH70KzZs3Rtm27EucRIq9906bNgLu76l/xHR2Lf9kUNXToUHTv3h179+7F0aNHsWzZMixZsgTBwcHo27f4r/sFtm3bBh8fH3h7e2PmzJmwsrKCjo4OFi1ahLt37xarb6TmgQCg3rYzMTFBdna22susDAYGym0o2AYeHqPh6ak6MXJ0zLu++MiRbViwwAc9enhj5MiZsLDI668tWxbh4cPi/VUZCn+pl/SAw6KDlTVB3X11xIjRGD1adT+2bdMGQMnX5BcsY/S772LskCGql9GiBW7+/XdeTFLVJ14rsv+WRBt3iJJIJNi9YwfOnjuHA7/9hpDjxzF+0iQs/+47nNuzB8ZlL6L4MtUsU8WonHed0inhfVp5WwX48OOPEbRpE6Z99BFcXFxgZmoKiUSC4aNGqTzbXZmf9SUpz/6piXjK8/n8ItSJfdvOnfDx84O3lxdmfvQRrCwt875bvv4ad1XcxIGIXm9avbdhwWUbpqamcHd3V1lHLodayUZFNGxoh2vX/oJcLlc6u/H337cV0wt069YDNjZ1sWfPTri6dkNY2O/47LMvSl2+vX3emQddXT306qW6feqqW7cu/Pz84Ofnh/j4eLzxxhtYuHAh+vbtW+KX4O7du9GoUSMEBwcr1fH391d7vSUtW51tB+T9cmhkZKQ4E1LYnTt3yly/hYUV9PUN8eBB8buYPXxYvKwoc3NLGBvXhFwuQ6dOpW+D0NDdqFevERYtUu6v9euV++tFnnoeGRmpdAYpKioKcrkc9nZ2il9Yiz50L1bFWY+KxPAicVtaWqJmzZqQyWTo3Vt1P0pyc4CUJyqnAYBl7dqoaWICmUwG9yKX+BRWkGyUxS7/1+PI6Gj0KnSJzvPnzxF9/z7atWih1nLKXE/9+gg9cwbPMjOVzm5EFbkzUnl16dwZXTp3xsLAQOzYuROjxo1DyLFjKPuWEUBkQoLSGYmoxETIhYB9rVoQQkAuBCITEtCi0E0YHqenIyUzE3Ya+CW/sILlRxWJMfHpUyRnZqq1jN179mDse+9h+VdfKcqysrIq/EBKu/xf2yMjI5XOCD958kRx9qMsFubmSCl0xqtAbAUeQGdZpw6MjY1xR8XdGW9HRkIqlaJBGWe8VbHLP8MTFR0Nh0Jn3hKTkhRnVSrD7v370cjeHsHbtil/t7zgk8J9hg6Fz9ChLxoeEb1kNDpmoyxOTk5wdHTEV199hYwit04EUOzWhZXNw8MTjx/HYffu/8Zi5Obm4ocfVsLExATdu7spyqVSKQYOHIxDhw7gp5+2Ijc3t9RLqADAysoKPXr0xMaNa/Ho0aNi09Vpn0wmQ2qRLzgrKyvUq1dPcdagRo0axeoA//1CVfgXqYiICISHh5e53gI1atQAUPwAWN1tp6OjAw8PD+zbtw/38q+1B/KexRISElLm+nV0dNCpkztOntyHJ0/+G7Py4EEUwsPLHuego6ODnj0HISxsD+7evV5senLyE6W6gHJ/3bgRgevXlfvL0DDvt+eMjJQy11/U6h9+UPp/5erVAIC+ffrA1NQUderUwclCt6IEgO+LzAOUvF1KUzBPaqr68xTQ0dGBt/cg7Nu3BzduFO9Hdd7LOjo6GOTpiT2HD+P67eLjO54kJpYrpo5t28Kydm2s2bpV6Y5Cm3btUnlQWFEebm54/vw5fix06ZdcLsdqFbcoVkdycnKxX7gL7t6To+YYjtVnzij9vzL/dtN9mzWDZ/PmAIBvTp9WqvN1/vvKK3+6pvRu3Bi6Uil+yL+FboFVRWIujY6OTrE+WrlqVYXP8rn37g09PT2sXL1aabnfFLmla2kcHRyQmpaGvwqNK3oUF4e9v/1W7nh0dHTw9ptvYv+hQ0qXUD6Oj8eO3bvRrUsXmJZj3FGB3m5u0NXVxQ8bNiiVr1qn+tLHilL53XLhAsLPnXuh5SYkJeF2VFSFxzIR0ctJq2c2pFIp1q9fj759+6JVq1YYN24c6tevj4cPHyI0NBSmpqbYv/+AxtY/frwvNmxYC19fH1y+fBENG9pj377dCA//E8uWfYOaNWsq1R88eBh++GElFizwR+vWbdC8edm/nK5YsRru7t3QuXMbjBs3Efb2jRAf/xgREeH4998HiIgofTBdeno6Gja0xeDBg9GuXTuYmJjg+PHjOH/+PJYvz3tmiJOTE3bu3Inp06ejU6dOMDExQf/+/dGvXz8EBwdj4MCB8PLyQnR0NNasWYOWLVuqTBBUcXR0hLm5OdasWYOaNWuiRo0acHZ2hoODQ5nb7sCBvG0XGBiII0eOoHv37vDz80Nubi5WrlyJVq1a4a+//iozhgkTAnDu3FFMmtQVAwdOhlwuw+7dq9CoUWtERl4pc34/v8W4dCkUEyc6Y8CAibC3b4m0tCT8/fclnD9/HCEhede5u7r2Q1hYMGbNGghXVy88ehSNffvWwN6+JTIz/+svAwMjODi0xIkTO9GgQVOYmtZCR6eOaNu67HEd0TExGODtjT4eHgg/exbbtm/HyBEj0K5d3uV4748fj8VLl+J9X190dHLCyVOn8LeKs0JOb7wBAPhizhwMHzYs/xbNA6Crb1Liujt0cAIA+Pt/gSFDhkNPTw9eXv3LjLnAggWL8ccfoeje3Rnjx09E8+YtkZychCtXLuH3348j7n5cmctYPHs2Qs+cgXP//pg4ciRaNmmCpJQUXLp+HcdPnUJSOZ57oKenhwUzZ+KDWbPQa9gwDOvfH9H37yNo1y40qsTrxr379EHn9u3x6fz5iIqJQfPGjfHrsWNIyk/0ynvGaPP27fh+3ToMHDAAjg4OSM/IwI9BQTA1NUU3V1egSLKpSnRSEgYEBaFPs2YIv3cP2y5dwsj27dGuXt6YmbFOTlgXEYGUzEy4NWqEc/fvY/PFi/Bu1QpvqhgnUJmsa9bEx926YfnJk4oYrz56hMN37qBOjRpq9Vc/Ly9s3bYNZmZmaNmiBcLPnsXxEycUt2EuL0tLS8yYPh2LlixBvwED4Nm3Ly5fuYLDR44UG9dXkuGDBuHzgAAMHD0aH33wAZ5lZuKHjRvR1NGxQoOiF/zf/+FYWBi69e0LvwkToKuri7VBQcjOzsbSefPKvTwAsLaywseTJmH5qlUYMHw4+ri74+r16zh8/Djq1K79Qmc3C+vn4YHgAwcwcNQoeHl4IDo2Fms2bkTL5s3V/m5RZVVQEAJXrEB0eDjsi4zDIaJXl9YfEduzZ0+Eh4dj/vz5WLVqFTIyMmBjYwNnZ2d8UI47aFSEkZERjhwJw5w5s7B9+2akpaWhadNmWLs2CGPG+BSr36WLK2xtG+DBg/ul3oWqsBYtWuL06Qv48stAbN26CUlJibC0tEK7dh0we/bcMuc3NjaGn58fjh49iuDgYMjlcjRu3Bjff/89Juff2cjPzw9XrlxBUFAQVqxYATs7O/Tv3x8+Pj6Ii4vD2rVrERISgpYtW2Lbtm345ZdfEBYWplb8enp62Lx5M2bPno1JkyYhNzcXQUFBcHBwUHvbtW3bFiEhIZg+fTrmzp0LW1tbBAYG4tGjR2olG82bO2H58sNYtWoGfvxxDqysGmDixHmIibmF2NjS74AEALVqWWP9+nMICpqHP/4IRnDw9zAzqw0Hh1bw81uiqOfl5YOkpDjs27cW586FwN6+JebO3YbQ0F9w6ZJyf82atR4rVnyI7777BM+f5yAg4Ad49plYZiw7d+zA3IAAzPrf/6Crq4upU6Zg2ZL/Ypg7Zw6eJCRg95492PXLL+jbpw8OHzwIqyLPjOjUqRPmBwZizbp1OBISkjegNPYeLK1LTjY6duwEf//5WL9+DY4ePQK5XI7bt9W/vtra2hqnT5/Dl1/Ow759wXj8+HvUrl0bLVq0woIFS8peAABrS0ucO3gQ81asQPDhw/h+yxbUtrBAq6ZNseR//1M7lgK+o0dDJpdj2Zo1mLlwIdo0b45fN27EnGXLyr2skujo6OC3LVvw8dy52Lx7d95Zzj594P/JJ+jq7Q0DPb1yLc+tWzecu3ABP//yCx7Hx8PMzAydnZywfeNG1Ffj2SAAsHPUKMw9ehSzDh+GrlSKqa6uWOblpZi+fvBgNKpVC5suXsTeGzdgU7MmZr/5Jvzzn5OgaUs8PWGsp4cfz53D8agouNjZ4ej776Pb99/DUI0xIt+uWAEdHR1s37EDWVlZ6OrqiuMhIfDw9KxwTAvmz4ehoSHWrFuH0LAwOHfujKOHD8NrwAC15q9dqxb2btuG6V98gc/8/eFgZ4dFc+ci8u7dCiUbrVq0wKlDhzB73jwsWrECcrkczk5O2LZuXbFnbJTHksBAGBsZ4cctW3D8jz/g0qkTjgYHo1ufPpX2xHCfUaMQFx+PtUFBCPn9d7Rs1gzb1q3DL/v2IazIGTUiIomozBF0GiCXA1U8xvilYmAAlDBOttrIyACuXSv/fJ9/7o3o6BvYtav4L/9VrYaxQNvW6t2mWVPkAnguq/oBzwUkuTnQL2XMRnWz78gRDHz/fVw+fhzti9yKtsLi44FCTzmvTlIyM2Hh748FHh74YsQIoNBDAF8KMhmg5piSV01KSgos7O2x4P/+D1/MmKGdICq7fy0tgUK3vCail1c1P4yl6iI7W/lL6v79SISHH0KHDj21Eg+9XjKLHCTJZDKsDAqCac2aaK7hy5JeRZkqrrn/Jv/ysJ6FnudCla/oexUAvskf99WzW7eqDoeISPuXURGpY/DgRvD09EG9eo0QFxeLvXt/gJ6ePkaP/kzbodFr4MM5c5CZlQUXJydk5+Qg+PBhnLlwAV/OmgVDA3WfjvH62Hn1KjZduADP5s1hoq+P0zEx+OnKFbzdtCm6VvD5JKSencHB2LRjBzzffhsmNWrg9Nmz+Gn3brzdqxe6dumi7fCI6DXEZINeCc7OfXDs2E9ISoqDnp4BWrd2waRJX6JBg+IPCySqbL26dsXydetw8MQJZGVno7G9PVbOn4+p48ZpO7SXUlsbG+hKpVgaFoa07GxYm5jg427dsMBD9fOGqPK0bdUKurq6WPrtt0hLT1cMGl/wf/+n7dCI6DXFMRsvOY7ZeDVwzMbrN2ZDQUcHyH+6+QurxmM2lFhZcczG64ZjNoheW9X8MJaIiIiIiLSFyQYREREREWkEkw0iIiIiItIIJhtERERERKQRTDZI6/T0AIlE21G8mOe5eTcz0Ka8LtTe/R6ERKrFtWuREHmvymBklDfgvLrLzARyc7UdhbJX/UPoZVfZ/Vvd75xCVI289HejArR/EKdNr8vnaXY2oOI5YK8UiURA1deprm7V3TSl1OPeqjiYkqnOuiRVtHqtKanTpdLyJw9paSXftac6fSBIparfFIaGQI0aVR8PkPfeLWlbVus3cBWRyVR/oUsk5etfqTTvg5WIXgmvRLJBRERERESvnmr0MxkREREREb1MmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQawWSDiIiIiIg0gskGERERERFpBJMNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUREREREGsFkg4iIiIiINILJBhERERERaQSTDSIiIiIi0ggmG0REREREpBFMNoiIiIiISCOYbBARERERkUYw2SAiIiIiIo1gskFERERERBrBZIOIiIiIiDSCyQYREREREWkEkw0iIiIiItIIJhtERERERKQRTDaIiIiIiEgjmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQawWSDiIiIiIg0gskGERERERFpBJMNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUREREREGsFkg4iIiIiINILJBhERERERaQSTDSIiIiIi0ggmG0REREREpBFMNoiIiIiISCOYbBARERERkUYw2SAiIiIiIo1gskFERERERBrBZIOIiIiIiDSCyQYREREREWkEkw0iIiIiItIIJhtERERERKQRTDaIiIiIiEgjmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQaoavtAIio+svKykJOTo62wyCiIvT19WFoaKjtMIioGmOyQUQalZWVBSMjBwBx2g6FiIqwsbFBdHQ0Ew4i0hgmG0SkUXlnNOIgkdyHVGoKqRSQSgFdXSj+lkoBiUT5/6J1VE0vXKek6QV1Spte8NLRKX26RFI8blVtKKlO4RhU1SmrD16WfpLIZYBcXvJLCCA3t/TpcnnJdQqml1Sn8HR16xStp2p64TolTS+oU9p0uRyQldFHL0E/pQmBBnFxyMnJYbJBRBrDZIOIqogpJBJTSCRQ+SrrALeiCUJpB/8VqVeVdV7WmMpMNko7QNZWncpclo5O5cUElD5dIslLGlTtNAXzA3kbpqiC+Ut6CVE1uz4RvdZUfDoRERERERG9OCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUREREREGsFkg4iIiIiINILP2SCiKpIGIVDiq/AjAwoULitpvoLHBcjlJT8XAlDvWR6lLUMqzVuGTFb69NKeV8GH+lXNw+pU1ila71V6qF9JyyorhtJ2OCGQVjU7PhG95phsEJFGCSFgYmKCjIwGkMnyjpsAIDtbu3EREWBiYgLBh/sRkQYx2SAijZJIJMjIyMD9+/dhamqq7XA0Ji0tDQ0aNGA7q4HXoY3Af+2USCTaDoWIqjEmG0RUJUxNTav1gVsBtrP6eB3aSESkaRwgTkREREREGsFkg4iIiIiINILJBhFplIGBAfz9/WFgYKDtUDSK7aw+Xoc2Aq9PO4lIuySCt6EgIiIiIiIN4JkNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUSVbuHChXB1dYWxsTHMzc3VmkcIgblz56Ju3bowMjKCu7s7IiMjNRvoC0hKSsKoUaNgamoKc3NzTJgwARkZGaXO07NnT0gkEqXXpEmTqihi9axevRr29vYwNDSEs7Mzzp07V2r9X375Bc2bN4ehoSHatGmDQ4cOVVGkL6Y87dy0aVOx7WZoaFiF0VbMyZMn0b9/f9SrVw8SiQT79u0rc56wsDC88cYbMDAwQOPGjbFp0yaNx0lE1RuTDSKqdDk5ORgyZAgmT56s9jxLly7Fd999hzVr1iAiIgI1atSAh4cHsrKyNBhpxY0aNQo3btzAsWPHcPDgQZw8eRK+vr5lzjdx4kQ8evRI8Vq6dGkVRKuenTt3Yvr06fD398elS5fQrl07eHh4ID4+XmX9M2fOYMSIEZgwYQIuX74Mb29veHt74/r161UcefmUt50AYGpqqrTdYmNjqzDiinn69CnatWuH1atXq1U/OjoaXl5eePPNN3HlyhVMmzYN77//PkJCQjQcKRFVa4KISEOCgoKEmZlZmfXkcrmwsbERy5YtU5SlpKQIAwMD8dNPP2kwwoq5efOmACDOnz+vKDt8+LCQSCTi4cOHJc7n5uYmPv744yqIsGI6d+4spkyZovhfJpOJevXqiUWLFqmsP3ToUOHl5aVU5uzsLD744AONxvmiyttOdd/HLzMAYu/evaXW+eyzz0SrVq2UyoYNGyY8PDw0GBkRVXc8s0FEWhcdHY24uDi4u7sryszMzODs7Izw8HAtRqZaeHg4zM3N0bFjR0WZu7s7pFIpIiIiSp13+/btqFOnDlq3bo3Zs2fj2bNnmg5XLTk5Obh48aLSNpBKpXB3dy9xG4SHhyvVBwAPD4+XcpsVqEg7ASAjIwN2dnZo0KAB3nnnHdy4caMqwq1Sr+L2JKKXn662AyAiiouLAwBYW1srlVtbWyumvUzi4uJgZWWlVKarq4tatWqVGu/IkSNhZ2eHevXq4a+//sLnn3+OO3fuIDg4WNMhlykhIQEymUzlNrh9+7bKeeLi4l6ZbVagIu1s1qwZNm7ciLZt2yI1NRVfffUVXF1dcePGDdja2lZF2FWipO2ZlpaGzMxMGBkZaSkyInqV8cwGEall1qxZxQbJFn2VdLD2qtB0G319feHh4YE2bdpg1KhR2LJlC/bu3Yu7d+9WYiuosrm4uOC9995D+/bt4ebmhuDgYFhaWmLt2rXaDo2I6KXHMxtEpJZPP/0UPj4+pdZp1KhRhZZtY2MDAHj8+DHq1q2rKH/8+DHat29foWVWhLpttLGxKTaYODc3F0lJSYq2qMPZ2RkAEBUVBUdHx3LHW5nq1KkDHR0dPH78WKn88ePHJbbJxsamXPVfBhVpZ1F6enro0KEDoqKiNBGi1pS0PU1NTXlWg4gqjMkGEanF0tISlpaWGlm2g4MDbGxscOLECUVykZaWhoiIiHLd0epFqdtGFxcXpKSk4OLFi3BycgIA/P7775DL5YoEQh1XrlwBAKUES1v09fXh5OSEEydOwNvbGwAgl8tx4sQJTJ06VeU8Li4uOHHiBKZNm6YoO3bsGFxcXKog4oqpSDuLkslkuHbtGjw9PTUYadVzcXEpduvil317EtErQNsj1Imo+omNjRWXL18WgYGBwsTERFy+fFlcvnxZpKenK+o0a9ZMBAcHK/5fvHixMDc3F/v37xd//fWXeOedd4SDg4PIzMzURhPK1KdPH9GhQwcREREhTp8+LZo0aSJGjBihmP7gwQPRrFkzERERIYQQIioqSsybN09cuHBBREdHi/3794tGjRqJHj16aKsJxfz888/CwMBAbNq0Sdy8eVP4+voKc3NzERcXJ4QQYsyYMWLWrFmK+n/++afQ1dUVX331lbh165bw9/cXenp64tq1a9pqglrK287AwEAREhIi7t69Ky5evCiGDx8uDA0NxY0bN7TVBLWkp6cr9j0A4uuvvxaXL18WsbGxQgghZs2aJcaMGaOo/88//whjY2Mxc+ZMcevWLbF69Wqho6Mjjhw5oq0mEFE1wGSDiCrd2LFjBYBir9DQUEUdACIoKEjxv1wuF3PmzBHW1tbCwMBA9O7dW9y5c6fqg1dTYmKiGDFihDAxMRGmpqZi3LhxSslUdHS0Upvv3bsnevToIWrVqiUMDAxE48aNxcyZM0VqaqqWWqDaypUrRcOGDYW+vr7o3LmzOHv2rGKam5ubGDt2rFL9Xbt2iaZNmwp9fX3RqlUr8dtvv1VxxBVTnnZOmzZNUdfa2lp4enqKS5cuaSHq8gkNDVW5Hxa0bezYscLNza3YPO3btxf6+vqiUaNGSvsoEVFFSIQQQiunVIiIiIiIqFrj3aiIiIiIiEgjmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQawWSDiIiIiIg0gskGERERERFpBJMNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijfh/uHZnOYpvv8AAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 29
  },
  {
   "cell_type": "markdown",
   "id": "a3ad9c13-cfd2-4cf7-a03f-322326839406",
   "metadata": {},
   "source": [
    "Indeed, \"boring\" and \"unoriginal\" are the words carrying the overall negative sentiment, hence they contribute most toward that decision, while \"great\" being a positive word, contributes negatively towards the classifier's decision!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
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}