movie_review_sentiment_analysis.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c2704d5-404f-4900-8faa-68136d446a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/amiya/miniconda3/envs/web_scrap/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from bs4 import BeautifulSoup\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0649cc-6610-49bc-893e-d1e2a97ee80f",
   "metadata": {},
   "source": [
    "# Data Acquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4b4c8907-3d70-40c2-9471-0f209fc6397b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"IMDB Dataset.csv\",) # loading the IMDB Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "033c0952-72e7-4e37-bf54-a2493cb8d086",
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment\n",
       "0  One of the other reviewers has mentioned that ...  positive\n",
       "1  A wonderful little production. <br /><br />The...  positive\n",
       "2  I thought this was a wonderful way to spend ti...  positive\n",
       "3  Basically there's a family where a little boy ...  negative\n",
       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # showing top 5 rows uisng df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bfb510e0-5966-4d29-b312-01131586a038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cdc474a",
   "metadata": {},
   "source": [
    "##### The dataset has 2000 rows and two columns and the column names are review and sentiment. \n",
    "* sentiments are etiher positive or negative"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19daab82-67d4-47ec-903c-cc74b303a61f",
   "metadata": {},
   "source": [
    "# Data Processing and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "393d70c6-46ca-4263-b95f-dbfccc08f548",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10101/1028110265.py:2: MarkupResemblesLocatorWarning:\n",
      "\n",
      "The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def clean_text(review:str):\n",
    "    text = BeautifulSoup(review, \"html.parser\")\n",
    "    return text.get_text()\n",
    "\n",
    "df['clean_text'] = df['review'].apply(clean_text) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1171bc24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 3)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.dropna(inplace=True) # drop if any NONE values are there in the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4e3aa57c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame after dropping rows:\n"
     ]
    },
    {
     "data": {
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       "<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>review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>clean_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>If you like original gut wrenching laughter yo...</td>\n",
       "      <td>positive</td>\n",
       "      <td>If you like original gut wrenching laughter yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>This a fantastic movie of three prisoners who ...</td>\n",
       "      <td>positive</td>\n",
       "      <td>This a fantastic movie of three prisoners who ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>What an absolutely stunning movie, if you have...</td>\n",
       "      <td>positive</td>\n",
       "      <td>What an absolutely stunning movie, if you have...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>The plot is about the death of little children...</td>\n",
       "      <td>negative</td>\n",
       "      <td>The plot is about the death of little children...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Protocol is an implausible movie whose only sa...</td>\n",
       "      <td>negative</td>\n",
       "      <td>Protocol is an implausible movie whose only sa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               review sentiment  \\\n",
       "9   If you like original gut wrenching laughter yo...  positive   \n",
       "14  This a fantastic movie of three prisoners who ...  positive   \n",
       "22  What an absolutely stunning movie, if you have...  positive   \n",
       "36  The plot is about the death of little children...  negative   \n",
       "46  Protocol is an implausible movie whose only sa...  negative   \n",
       "\n",
       "                                           clean_text  \n",
       "9   If you like original gut wrenching laughter yo...  \n",
       "14  This a fantastic movie of three prisoners who ...  \n",
       "22  What an absolutely stunning movie, if you have...  \n",
       "36  The plot is about the death of little children...  \n",
       "46  Protocol is an implausible movie whose only sa...  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = df['clean_text'].apply(lambda x: len(x.strip())) > 512\n",
    "## Drop any review which has a len greater that 512 as the model token size in 512\n",
    "df = df[~condition]\n",
    "print(\"\\nDataFrame after dropping rows:\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2974b252",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, row in df.iterrows():\n",
    "    print(len(row['clean_text']))\n",
    "    # pred(row['clean_text'])[1] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902876bb",
   "metadata": {},
   "source": [
    "##### class distribution of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6a30c3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentiment\n",
       "positive    2929\n",
       "negative    2436\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "e809a3ba",
   "metadata": {},
   "outputs": [
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          "positive",
          "negative"
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   "source": [
    "fig = px.bar(df, x='sentiment', color='sentiment',\n",
    "              title=\"Bar chart of postive and negative sentiments\", \n",
    "              width=500)\n",
    "fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e57bbbb",
   "metadata": {},
   "source": [
    "` we can see here that there are equal num of postive and negative review sentiments available in the dataset`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1915867b",
   "metadata": {},
   "source": [
    "#### visualizing distribution using pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d5f89cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'positive': 2929, 'negative': 2436})"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "counter = Counter(df['sentiment'])\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "aba84785",
   "metadata": {},
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         "text": "Sentiment Pie Chart"
        }
       }
      }
     },
     "metadata": {},
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   ],
   "source": [
    "data_list = [{'sentiment': key, 'count': value} for key, value in counter.items()]\n",
    "fig = px.pie(data_list, values='count', names='sentiment', title='Sentiment Pie Chart')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "143725a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Adding a binary value to each calss to use accuracy metric\n",
    "df['class_int_value'] = df['sentiment'].apply(lambda x: 1 if x == 'positive' else 0)\n",
    "actual_y = df['class_int_value'].tolist()\n",
    "actual_y[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25752f7",
   "metadata": {},
   "source": [
    "####  Sentiment Analysis Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52e4dc6d-8117-4c15-a92e-bb9fabe97005",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "tokenizer_config.json: 100%|██████████| 48.0/48.0 [00:00<00:00, 2.83kB/s]\n",
      "vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 3.05MB/s]\n",
      "config.json: 100%|██████████| 629/629 [00:00<00:00, 216kB/s]\n",
      "model.safetensors: 100%|██████████| 268M/268M [03:02<00:00, 1.47MB/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'POSITIVE'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification\n",
    "\n",
    "tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\") # Loading the bert model\n",
    "model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\") # loading the tokenizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699a4ee6",
   "metadata": {},
   "source": [
    "### Prediction of setimentns From the clean_text column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8698e502-a0cd-4d96-b6ae-67d0e7f423a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "##predict the sentiments \n",
    "def pred(text):\n",
    "    try:\n",
    "        inputs = tokenizer(text, return_tensors=\"pt\")  # Tokenize the input text using the specified tokenizer\n",
    "\n",
    "        # Run the model in inference mode\n",
    "        with torch.inference_mode():\n",
    "            logits = model(**inputs).logits # Pass the tokenized input to the model and obtain logits\n",
    "\n",
    "        predicted_class_id = logits.argmax().item() # Find the index of the class with the highest probability\n",
    "\n",
    "        pred_sentiment = model.config.id2label[predicted_class_id].lower() # Map the predicted class index to the corresponding label\n",
    "\n",
    "        return (pred_sentiment, 1 if pred_sentiment == 'positive' else 0)\n",
    "\n",
    "    except Exception as e:\n",
    "        pass\n",
    "        print(f\"An error occurred: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e62b9863-9e53-45b8-9117-0115644453dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_y = []\n",
    "for idx, row in df.iterrows():\n",
    "    pred_y.append(pred(row['clean_text'])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "8ee9ff7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('              precision    recall  f1-score   support\\n'\n",
      " '\\n'\n",
      " '           0       0.90      0.89      0.90      2436\\n'\n",
      " '           1       0.91      0.92      0.92      2929\\n'\n",
      " '\\n'\n",
      " '    accuracy                           0.91      5365\\n'\n",
      " '   macro avg       0.91      0.91      0.91      5365\\n'\n",
      " 'weighted avg       0.91      0.91      0.91      5365\\n')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from pprint import pprint\n",
    "\n",
    "pprint(classification_report(actual_y, pred_y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d80535",
   "metadata": {},
   "source": [
    "` The classification report provides an evaluation of a binary classification model's performance. With an accuracy of 91%, the model demonstrates high precision and recall for both classes (0 and 1), indicating effective predictions. The weighted average F1-score of 0.91 reflects the model's balanced performance across classes, yielding robust overall predictions on a dataset of 5365 instances. `"
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  },
  {
   "cell_type": "markdown",
   "id": "cb8219c1",
   "metadata": {},
   "source": [
    "# Results Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "361c3caa",
   "metadata": {},
   "outputs": [
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   "source": [
    "pre_counter = dict(Counter(pred_y))\n",
    "print(pre_counter)\n",
    "pre_counter['positve'] = pre_counter[1]\n",
    "pre_counter['negative'] = pre_counter[0]\n",
    "del pre_counter[0]\n",
    "del pre_counter[1]\n",
    "data_list = [{'sentiment': key, 'count': value} for key, value in pre_counter.items()]\n",
    "fig = px.pie(data_list, values='count', names='sentiment', title='prediction Sentiment Pie Chart')\n",
    "fig.show()\n"
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   "source": [
    "#### In the prediction chart we can see that the postive  prediction has fallen down to 45% to 44% than the actual pie chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1d9fd0",
   "metadata": {},
   "source": [
    "# Make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e8e3f5de",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive\n"
     ]
    }
   ],
   "source": [
    "input_text = \"what is photosynthesis\"\n",
    "print(pred(input_text)[0])"
   ]
  },
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   "id": "264ca15c",
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   "source": [
    "# Write-up\n",
    "` In the project we first import the necessary libraries including pandas, numpy. plotly, beautufulsoup and`  ` sentence transformers. We then import the IMDB dataset in pandas which contains 50000 movie reviews and the the sentiment of the reviews. The reviews are here input variable and the sentiment is a binary target or output class positive and negative. we then applied the necessary preprocessing steps followed:`\n",
    "* Remove html tags from the review texts using beautifulsoup \n",
    "* Remove any None contain rows\n",
    "* Remove rows where the text length is more than 512 as our model maximum input token size is 512\n",
    "\n",
    "`After applying the preprocessing steps we visualized the data using ploty to see the distribution of the classes and we found out there are around 54% pos class and 45% negative class is present. After that we used the dataset to predict hwo our model is doing. I used here a pretrained huggingface model which is distilbert-base-uncased-finetuned-sst-2-english using the transformer library from huggingface and predict the texts using the torch.inference_mode(). After that I stored the prediction result in a list named pred_y to plot the classification report using sklearn library.I got 91% accurary using this model and after that I ploted the prediction result in a pie chart and I got that the model preformed quiet well.`"
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