research/sentiment_analysis_research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'label': 'LABEL_0', 'score': 0.965427041053772}, {'label': 'LABEL_1', 'score': 0.028996318578720093}, {'label': 'LABEL_2', 'score': 0.005576656199991703}]\n"
     ]
    }
   ],
   "source": [
    "model = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-roberta-base-sentiment\", top_k=3)\n",
    "result = model(\"I hate you\")[0]\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "class SentimentAnalysis:\n",
    "    def __init__(self):\n",
    "        self.model = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-roberta-base-sentiment\", top_k=3)\n",
    "        self.labels = {\"LABEL_0\": \"negative\", \"LABEL_1\": \"neutral\", \"LABEL_2\": \"positive\"}\n",
    "\n",
    "    def predict(self, text):\n",
    "        result = self.model(text)[0]\n",
    "        results = {self.labels[res[\"label\"]]: res['score'] for res in result}\n",
    "        return results"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'negative': 0.965427041053772, 'neutral': 0.028996318578720093, 'positive': 0.005576656199991703}\n"
     ]
    }
   ],
   "source": [
    "sentiment_analysis = SentimentAnalysis()\n",
    "result = sentiment_analysis.predict(\"I hate you\")\n",
    "print(result)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset glue (/Users/pandermatt/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/3 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "accf0b72f4cd48f6ad69b8bd0d8b056f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67349\n",
      "{'sentence': ['hide new secretions from the parental units ', 'contains no wit , only labored gags ', 'that loves its characters and communicates something rather beautiful about human nature ', 'remains utterly satisfied to remain the same throughout ', 'on the worst revenge-of-the-nerds clichés the filmmakers could dredge up '], 'label': [0, 0, 1, 0, 0], 'idx': [0, 1, 2, 3, 4]}\n"
     ]
    }
   ],
   "source": [
    "dataset = load_dataset(\"glue\", \"sst2\")\n",
    "\n",
    "print(len(dataset[\"train\"]))\n",
    "\n",
    "print(dataset[\"train\"][:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1}\n"
     ]
    }
   ],
   "source": [
    "print(set(dataset[\"train\"][\"label\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'neutral': 0.6850253343582153, 'negative': 0.28855881094932556, 'positive': 0.026415809988975525}\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "result = sentiment_analysis.predict(dataset[\"train\"][0][\"sentence\"])\n",
    "print(result)\n",
    "print(dataset[\"train\"][0][\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sentence': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['negative', 'positive'], id=None), 'idx': Value(dtype='int32', id=None)}\n",
      "67349\n",
      "{0, 1}\n"
     ]
    }
   ],
   "source": [
    "print(dataset[\"train\"].features)\n",
    "# number of unique sentiments in the dataset\n",
    "print(len(dataset[\"train\"]))\n",
    "# number of rows for each sentiment in the dataset\n",
    "print(set(dataset[\"train\"][\"label\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sentence': ['hide new secretions from the parental units ', 'contains no wit , only labored gags ', 'that loves its characters and communicates something rather beautiful about human nature '], 'label': [0, 0, 1], 'idx': [0, 1, 2]}\n"
     ]
    }
   ],
   "source": [
    "print(dataset[\"train\"].features)\n",
    "\n",
    "print(dataset[\"train\"][:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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'neutral', 'negative', 'neutral', 'positive', 'neutral', 'positive', 'neutral', 'positive', 'negative', 'positive', 'neutral', 'neutral', 'negative', 'negative', 'positive', 'positive', 'neutral', 'positive', 'positive', 'neutral', 'neutral', 'negative', 'positive', 'negative', 'neutral', 'positive', 'negative', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative', 'positive', 'neutral', 'positive', 'positive', 'negative', 'neutral', 'positive', 'negative', 'neutral', 'negative', 'negative', 'neutral', 'neutral', 'neutral', 'negative', 'positive', 'neutral', 'positive', 'neutral', 'negative', 'neutral', 'negative', 'positive']\n"
     ]
    }
   ],
   "source": [
    "y_true = []\n",
    "y_pred = []\n",
    "i = 0\n",
    "for row in dataset[\"train\"]:\n",
    "    if i == 1000:\n",
    "        break\n",
    "    i += 1\n",
    "    result = sentiment_analysis.predict(row[\"sentence\"])\n",
    "    if row[\"label\"] == 0:\n",
    "        y_true.append(\"negative\")\n",
    "    elif row[\"label\"] == 1:\n",
    "        y_true.append(\"positive\")\n",
    "    else:\n",
    "        print(\"error\")\n",
    "        break\n",
    "    y_pred.append(max(result, key=result.get))\n",
    "\n",
    "print(y_true)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.554\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.93      0.63      0.75       458\n",
      "     neutral       0.00      0.00      0.00         0\n",
      "    positive       0.97      0.49      0.65       542\n",
      "\n",
      "    accuracy                           0.55      1000\n",
      "   macro avg       0.63      0.37      0.47      1000\n",
      "weighted avg       0.95      0.55      0.70      1000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/conda-m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/conda-m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/conda-m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_true, y_pred))\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true, y_pred, labels=[\"positive\", \"negative\", \"neutral\"])\n",
    "\n",
    "ax = plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax=ax, fmt=\"d\", cmap=\"Blues\")\n",
    "\n",
    "ax.set_xlabel(\"Predicted labels\")\n",
    "ax.set_ylabel(\"True labels\")\n",
    "ax.set_title(\"Confusion Matrix\")\n",
    "ax.xaxis.set_ticklabels([\"positive\", \"negative\", \"neutral\"])\n",
    "ax.yaxis.set_ticklabels([\"positive\", \"negative\", \"neutral\"])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Ignoring neutral sentiment"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "y_true = []\n",
    "y_pred = []\n",
    "i = 0\n",
    "for row in dataset[\"train\"]:\n",
    "    if i == 1000:\n",
    "        break\n",
    "    i += 1\n",
    "    result = sentiment_analysis.predict(row[\"sentence\"])\n",
    "    if row[\"label\"] == 0:\n",
    "        y_true.append(\"negative\")\n",
    "    elif row[\"label\"] == 1:\n",
    "        y_true.append(\"positive\")\n",
    "    else:\n",
    "        print(\"error\")\n",
    "        break\n",
    "    if max(result, key=result.get) == \"neutral\":\n",
    "        y_pred.append(sorted(result, key=result.get, reverse=True)[1])\n",
    "    else:\n",
    "        y_pred.append(max(result, key=result.get))\n",
    "\n",
    "print(y_true)\n",
    "print(y_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.87\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.82      0.92      0.87       458\n",
      "    positive       0.92      0.83      0.87       542\n",
      "\n",
      "    accuracy                           0.87      1000\n",
      "   macro avg       0.87      0.87      0.87      1000\n",
      "weighted avg       0.88      0.87      0.87      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_true, y_pred))\n",
    "print(classification_report(y_true, y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true, y_pred, labels=[\"positive\", \"negative\"])\n",
    "\n",
    "ax = plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax=ax, fmt=\"d\", cmap=\"Blues\")\n",
    "\n",
    "ax.set_xlabel(\"Predicted labels\")\n",
    "ax.set_ylabel(\"True labels\")\n",
    "ax.set_title(\"Confusion Matrix\")\n",
    "ax.xaxis.set_ticklabels([\"positive\", \"negative\"])\n",
    "ax.yaxis.set_ticklabels([\"positive\", \"negative\"])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}