IMDB_Sentiment_Analysis_using_LSTM.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/pulkitpathak99/IMDB-Sentiment-Analysis-using-LSTM/blob/main/IMDB_Sentiment_Analysis_using_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
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        },
        "id": "YJOogeY5kH87",
        "outputId": "795c7aea-6b83-492b-e5e5-32963a5d469d"
      },
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.7.4)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dependency** **Installation**"
      ],
      "metadata": {
        "id": "CKSB0T6bkynS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import json # to access the configurational file\n",
        "\n",
        "from zipfile import ZipFile #to extract kaggle dataset file\n",
        "import pandas as pd #loading dataset csv into pandas dataframe\n",
        "from sklearn.model_selection import train_test_split #to split dataset into training and testing set\n",
        "from tensorflow.keras.models import Sequential #keras provide API which makes it easier to create neural networks , we'll use layers like embedding\n",
        "from tensorflow.keras.layers import Input, Dense, Embedding , LSTM # dense layer -> fully connected layer , embedding layer -> first layer and then LSTM\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer #maps words to certain integers\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences #check if all input data is in same shape"
      ],
      "metadata": {
        "id": "PZmswvlOkske"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Collecting the data -> Kaggle API**"
      ],
      "metadata": {
        "id": "Bod-Zx7ZnMTe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kaggle_dict= json.load(open(\"kaggle.json\"))\n",
        "\n"
      ],
      "metadata": {
        "id": "izOSiK3ym6po"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#set up kaggle API key as environment variables as i don't want to risk my kaggle credentials in my code\n",
        "os.environ[\"KAGGLE_USERNAME\"]= kaggle_dict[\"username\"]\n",
        "os.environ[\"KAGGLE_KEY\"]= kaggle_dict[\"key\"]\n"
      ],
      "metadata": {
        "id": "JDuI-1fFpNAk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d lakshmi25npathi/imdb-dataset-of-50k-movie-reviews"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZY7yP97piRe",
        "outputId": "9c9ffa52-0f37-4d39-8512-d4fc6265c60f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews\n",
            "License(s): other\n",
            "Downloading imdb-dataset-of-50k-movie-reviews.zip to /content\n",
            " 97% 25.0M/25.7M [00:00<00:00, 131MB/s] \n",
            "100% 25.7M/25.7M [00:00<00:00, 122MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AnA19hgqQzy",
        "outputId": "fdf159c2-b7fe-489d-9288-c8c3154d730b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "imdb-dataset-of-50k-movie-reviews.zip  kaggle.json  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#unzip the file\n",
        "with ZipFile('imdb-dataset-of-50k-movie-reviews.zip') as zip_ref:\n",
        "  zip_ref.extractall()"
      ],
      "metadata": {
        "id": "7sKW-19UqXy0"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKJLFNUzqbgN",
        "outputId": "ecc34117-e32c-4009-d2a9-c7bd47ecc879"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'IMDB Dataset.csv'   imdb-dataset-of-50k-movie-reviews.zip   kaggle.json   sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/IMDB Dataset.csv')"
      ],
      "metadata": {
        "id": "VlO4TDl5qvIq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape #prints row and col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdNQtuixq7v0",
        "outputId": "81b5c145-e1e9-4e36-bcac-b6675affa5b9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head() #prints first 5 rows gives idea about the data\n"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UytHHjPQq_KU",
        "outputId": "aad36401-2304-4745-df46-038aff0b9faf"
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      "execution_count": 16,
      "outputs": [
        {
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            "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"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49582,\n        \"samples\": [\n          \"\\\"Soul Plane\\\" is a horrible attempt at comedy that only should appeal people with thick skulls, bloodshot eyes and furry pawns. <br /><br />The plot is not only incoherent but also non-existent, acting is mostly sub sub-par with a gang of highly moronic and dreadful characters thrown in for bad measure, jokes are often spotted miles ahead and almost never even a bit amusing. This movie lacks any structure and is full of racial stereotypes that must have seemed old even in the fifties, the only thing it really has going for it is some pretty ladies, but really, if you want that you can rent something from the \\\"Adult\\\" section. OK?<br /><br />I can hardly see anything here to recommend since you'll probably have a lot a better and productive time chasing rats with a sledgehammer or inventing waterproof teabags or whatever.<br /><br />2/10\",\n          \"Guest from the Future tells a fascinating story of time travel, friendship, battle of good and evil -- all with a small budget, child actors, and few special effects. Something for Spielberg and Lucas to learn from. ;) A sixth-grader Kolya \\\"Nick\\\" Gerasimov finds a time machine in the basement of a decrepit building and travels 100 years into the future. He discovers a near-perfect, utopian society where robots play guitars and write poetry, everyone is kind to each other and people enjoy everything technology has to offer. Alice is the daughter of a prominent scientist who invented a device called Mielophone that allows to read minds of humans and animals. The device can be put to both good and bad use, depending on whose hands it falls into. When two evil space pirates from Saturn who want to rule the universe attempt to steal Mielophone, it falls into the hands of 20th century school boy Nick. With the pirates hot on his tracks, he travels back to his time, followed by the pirates, and Alice. Chaos, confusion and funny situations follow as the luckless pirates try to blend in with the earthlings. Alice enrolls in the same school Nick goes to and demonstrates superhuman abilities in PE class. The catch is, Alice doesn't know what Nick looks like, while the pirates do. Also, the pirates are able to change their appearance and turn literally into anyone. (Hmm, I wonder if this is where James Cameron got the idea for Terminator...) Who gets to Nick -- and Mielophone -- first? Excellent plot, non-stop adventures, and great soundtrack. I wish Hollywood made kid movies like this one...\",\n          \"\\\"National Treasure\\\" (2004) is a thoroughly misguided hodge-podge of plot entanglements that borrow from nearly every cloak and dagger government conspiracy clich\\u00e9 that has ever been written. The film stars Nicholas Cage as Benjamin Franklin Gates (how precious is that, I ask you?); a seemingly normal fellow who, for no other reason than being of a lineage of like-minded misguided fortune hunters, decides to steal a 'national treasure' that has been hidden by the United States founding fathers. After a bit of subtext and background that plays laughably (unintentionally) like Indiana Jones meets The Patriot, the film degenerates into one misguided whimsy after another \\u0096 attempting to create a 'Stanley Goodspeed' regurgitation of Nicholas Cage and launch the whole convoluted mess forward with a series of high octane, but disconnected misadventures.<br /><br />The relevancy and logic to having George Washington and his motley crew of patriots burying a king's ransom someplace on native soil, and then, going through the meticulous plan of leaving clues scattered throughout U.S. currency art work, is something that director Jon Turteltaub never quite gets around to explaining. Couldn't Washington found better usage for such wealth during the start up of the country? Hence, we are left with a mystery built on top of an enigma that is already on shaky ground by the time Ben appoints himself the new custodian of this untold wealth. Ben's intentions are noble \\u0096 if confusing. He's set on protecting the treasure. For who and when?\\u0085your guess is as good as mine.<br /><br />But there are a few problems with Ben's crusade. First up, his friend, Ian Holmes (Sean Bean) decides that he can't wait for Ben to make up his mind about stealing the Declaration of Independence from the National Archives (oh, yeah \\u0096 brilliant idea!). Presumably, the back of that famous document holds the secret answer to the ultimate fortune. So Ian tries to kill Ben. The assassination attempt is, of course, unsuccessful, if overly melodramatic. It also affords Ben the opportunity to pick up, and pick on, the very sultry curator of the archives, Abigail Chase (Diane Kruger). She thinks Ben is clearly a nut \\u0096 at least at the beginning. But true to action/romance form, Abby's resolve melts quicker than you can say, \\\"is that the Hope Diamond?\\\" The film moves into full X-File-ish mode, as the FBI, mistakenly believing that Ben is behind the theft, retaliate in various benign ways that lead to a multi-layering of action sequences reminiscent of Mission Impossible meets The Fugitive. Honestly, don't those guys ever get 'intelligence' information that is correct? In the final analysis, \\\"National Treasure\\\" isn't great film making, so much as it's a patchwork rehash of tired old bits from other movies, woven together from scraps, the likes of which would make IL' Betsy Ross blush.<br /><br />The Buena Vista DVD delivers a far more generous treatment than this film is deserving of. The anamorphic widescreen picture exhibits a very smooth and finely detailed image with very rich colors, natural flesh tones, solid blacks and clean whites. The stylized image is also free of blemishes and digital enhancements. The audio is 5.1 and delivers a nice sonic boom to your side and rear speakers with intensity and realism. Extras include a host of promotional junket material that is rather deep and over the top in its explanation of how and why this film was made. If only, as an audience, we had had more clarification as to why Ben and co. were chasing after an illusive treasure, this might have been one good flick. Extras conclude with the theatrical trailer, audio commentary and deleted scenes. Not for the faint-hearted \\u0096 just the thick-headed.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
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    {
      "cell_type": "code",
      "source": [
        "data.tail()"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "3QFGiDGGrDef",
        "outputId": "761f2189-9a63-4697-ffc6-54784e454898"
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      "outputs": [
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              "                                                  review sentiment\n",
              "49995  I thought this movie did a down right good job...  positive\n",
              "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
              "49997  I am a Catholic taught in parochial elementary...  negative\n",
              "49998  I'm going to have to disagree with the previou...  negative\n",
              "49999  No one expects the Star Trek movies to be high...  negative"
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "  }\n",
              "\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0bf3179-12ff-41fe-92f5-b4e4f72435a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Bad plot, bad dialogue, bad acting, idiotic directing, the annoying porn groove soundtrack that ran continually over the overacted script, and a crappy copy of the VHS cannot be redeemed by consuming liquor. Trust me, because I stuck this turkey out to the end. It was so pathetically bad all over that I had to figure it was a fourth-rate spoof of Springtime for Hitler.<br /><br />The girl who played Janis Joplin was the only faint spark of interest, and that was only because she could sing better than the original.<br /><br />If you want to watch something similar but a thousand times better, then watch Beyond The Valley of The Dolls.\",\n          \"No one expects the Star Trek movies to be high art, but the fans do expect a movie that is as good as some of the best episodes. Unfortunately, this movie had a muddled, implausible plot that just left me cringing - this is by far the worst of the nine (so far) movies. Even the chance to watch the well known characters interact in another movie can't save this movie - including the goofy scenes with Kirk, Spock and McCoy at Yosemite.<br /><br />I would say this movie is not worth a rental, and hardly worth watching, however for the True Fan who needs to see all the movies, renting this movie is about the only way you'll see it - even the cable channels avoid this movie.\",\n          \"I am a Catholic taught in parochial elementary schools by nuns, taught by Jesuit priests in high school & college. I am still a practicing Catholic but would not be considered a \\\"good Catholic\\\" in the church's eyes because I don't believe certain things or act certain ways just because the church tells me to.<br /><br />So back to the movie...its bad because two people are killed by this nun who is supposed to be a satire as the embodiment of a female religious figurehead. There is no comedy in that and the satire is not done well by the over acting of Diane Keaton. I never saw the play but if it was very different from this movies then it may be good.<br /><br />At first I thought the gun might be a fake and the first shooting all a plan by the female lead of the four former students as an attempt to demonstrate Sister Mary's emotional and intellectual bigotry of faith. But it turns out the bullets were real and the story has tragedy...the tragedy of loss of life (besides the two former students...the lives of the aborted babies, the life of the student's mom), the tragedy of dogmatic authority over love of people, the tragedy of organized religion replacing true faith in God. This is what is wrong with today's Islam, and yesterday's Judaism and Christianity.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"sentiment\"].value_counts()"
      ],
      "metadata": {
        "colab": {
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          "height": 178
        },
        "id": "Gm3gl3I1rV33",
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      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "sentiment\n",
              "positive    25000\n",
              "negative    25000\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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    {
      "cell_type": "code",
      "source": [
        "data.replace({\"sentiment\" : {\"positive\":1 , \"negative\": 0}}, inplace=True) #positive is now changed inplacely to 1 while negative is to 0"
      ],
      "metadata": {
        "id": "Q6Jxa55Ore1K"
      },
      "execution_count": 19,
      "outputs": []
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      "cell_type": "code",
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        "data[\"sentiment\"].value_counts()"
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        "id": "_g7hIzgBr1lk",
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      "execution_count": 20,
      "outputs": [
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              "        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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split data into training and testing\n",
        "train_data, test_data= train_test_split(data, test_size=0.2, random_state=42)\n",
        "print(train_data.shape)\n",
        "test_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHH2N7bxsDoi",
        "outputId": "a1a0f857-afd7-455d-9fa3-ce24736a537e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(40000, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing**"
      ],
      "metadata": {
        "id": "AOFsbF7HtS9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer= Tokenizer(num_words=5000) #used to convert the words to integer\n",
        "tokenizer.fit_on_texts(train_data[\"review\"])\n",
        "X_train=pad_sequences(tokenizer.texts_to_sequences(train_data[\"review\"]), maxlen=200) #Words are mapped based on their frequency in the dataset\n",
        "X_test=pad_sequences(tokenizer.texts_to_sequences(test_data[\"review\"]), maxlen=200)"
      ],
      "metadata": {
        "id": "ga2cXwQFspVe"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AIq5hMH1v3nJ",
        "outputId": "fbcacc3a-9ce8-4e63-ae77-a850454d62d5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1935    1 1200 ...  205  351 3856]\n",
            " [   3 1651  595 ...   89  103    9]\n",
            " [   0    0    0 ...    2  710   62]\n",
            " ...\n",
            " [   0    0    0 ... 1641    2  603]\n",
            " [   0    0    0 ...  245  103  125]\n",
            " [   0    0    0 ...   70   73 2062]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBYsoZsBv7qK",
        "outputId": "d05418d1-5ec1-43f1-825e-866b7a309295"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   0    0    0 ...  995  719  155]\n",
            " [  12  162   59 ...  380    7    7]\n",
            " [   0    0    0 ...   50 1088   96]\n",
            " ...\n",
            " [   0    0    0 ...  125  200 3241]\n",
            " [   0    0    0 ... 1066    1 2305]\n",
            " [   0    0    0 ...    1  332   27]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train= train_data[\"sentiment\"]\n",
        "Y_test= test_data[\"sentiment\"]"
      ],
      "metadata": {
        "id": "qL4jE706wU73"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjOR9Ho4xLV2",
        "outputId": "a62e0654-1dae-4629-fc37-7b7dbc4fbb1c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "39087    0\n",
            "30893    0\n",
            "45278    1\n",
            "16398    0\n",
            "13653    0\n",
            "        ..\n",
            "11284    1\n",
            "44732    1\n",
            "38158    0\n",
            "860      1\n",
            "15795    1\n",
            "Name: sentiment, Length: 40000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3qy1Cn9xPbU",
        "outputId": "c40d35f4-1bf6-4523-9cad-f8a86be5b948"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33553    1\n",
            "9427     1\n",
            "199      0\n",
            "12447    1\n",
            "39489    0\n",
            "        ..\n",
            "28567    0\n",
            "25079    1\n",
            "18707    1\n",
            "15200    0\n",
            "5857     1\n",
            "Name: sentiment, Length: 10000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LSTM - Long Short Term Memory**"
      ],
      "metadata": {
        "id": "-2ahE_bzxin3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#In LSTM, when provided with input the model should not look for all the data to provide output instead it should have memory that it is aware of the trend\n",
        "# model building\n",
        "model = Sequential()\n",
        "model.add(Input(shape=(200,)))\n",
        "model.add(Embedding(input_dim=5000, output_dim=128, input_length=200))\n",
        "model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "model.summary()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "nn988siGxRiw",
        "outputId": "624d6bcb-20c7-4043-f57f-eb31fe6b3186"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_7 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m640,000\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_7 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m131,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">640,000</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">131,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m771,713\u001b[0m (2.94 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">771,713</span> (2.94 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m771,713\u001b[0m (2.94 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">771,713</span> (2.94 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#compile the model\n",
        "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "MZm5JjI5yhvj"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the model\n",
        "model.fit(X_train, Y_train, epochs=5, batch_size=64, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMT4jRXA1_2R",
        "outputId": "b35dff4a-c3e0-4600-c89e-b1b846e2b747"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 335ms/step - accuracy: 0.7287 - loss: 0.5331 - val_accuracy: 0.8238 - val_loss: 0.4054\n",
            "Epoch 2/5\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 341ms/step - accuracy: 0.8587 - loss: 0.3416 - val_accuracy: 0.8303 - val_loss: 0.3725\n",
            "Epoch 3/5\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 342ms/step - accuracy: 0.8719 - loss: 0.3139 - val_accuracy: 0.8687 - val_loss: 0.3251\n",
            "Epoch 4/5\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 305ms/step - accuracy: 0.8982 - loss: 0.2597 - val_accuracy: 0.8583 - val_loss: 0.3473\n",
            "Epoch 5/5\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 301ms/step - accuracy: 0.9076 - loss: 0.2400 - val_accuracy: 0.8786 - val_loss: 0.3148\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7e5578392710>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**"
      ],
      "metadata": {
        "id": "gRg5EYyt6FN_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss,accuracy= model.evaluate(X_test, Y_test)\n",
        "print(f\"Test Loss: {loss}\")\n",
        "print(f\"Test Accuracy: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkK1UYKa5r5N",
        "outputId": "edf66473-fe54-41b0-ebe6-b7648ef649a9"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 121ms/step - accuracy: 0.8859 - loss: 0.3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Building it into predictive system**"
      ],
      "metadata": {
        "id": "nlpe_Xj36yn1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predictive_sentiment(review):\n",
        "  #tokenize and pad review\n",
        "  sequence= tokenizer.texts_to_sequences([review])\n",
        "  padded_sequence= pad_sequences(sequence, maxlen=200)\n",
        "  prediction= model.predict(padded_sequence)\n",
        "  sentiment= \"positive\" if prediction[0][0]>0.5 else \"negative\"\n",
        "  return sentiment"
      ],
      "metadata": {
        "id": "EhRujiFR6YFs"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_review= \"The movie lost direction in the middle of the plot, humour was mediocre and characters had little to no development!\"\n",
        "sentiment= predictive_sentiment(new_review)\n",
        "print(f\"The Sentiment for this review is: {sentiment}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqGdxnYZ6_lD",
        "outputId": "d935adeb-f7a1-46ff-dba9-cc6bd87b0ba3"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
            "The Sentiment for this review is: negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_review= \"The movie was boring!\"\n",
        "sentiment= predictive_sentiment(new_review)\n",
        "print(f\"The Sentiment for this review is: {sentiment}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiBRfKLl9oW0",
        "outputId": "cfee5215-aa3a-48d2-da5a-d1a7ac27b1ad"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step\n",
            "The Sentiment for this review is: negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_review= \"Can't wait for the second part!\"\n",
        "sentiment= predictive_sentiment(new_review)\n",
        "print(f\"The Sentiment for this review is: {sentiment}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtzVEZCB-kLp",
        "outputId": "223e2120-ca44-4ce2-d69b-b7e692ecf43f"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step\n",
            "The Sentiment for this review is: positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4AE-1ZqbCJgo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}