Movie_Reviews_through_Sentiment_Analysis_in_NLP.ipynb

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        "<a href=\"https://colab.research.google.com/github/hussain0048/Projects-/blob/master/Movie_Reviews_through_Sentiment_Analysis_in_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# **📚Table of Content**"
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Introduction\n",
        "2.   Import necessary libraries\n",
        "3.   Load Dataset\n",
        "4.   Data Preprocessing\n",
        "5.   Model Training\n",
        "6-   Model Testing\n",
        "7.   Model saving\n",
        "8.   Performance Checking\n",
        "\n"
      ],
      "metadata": {
        "id": "yHmMqIDemLR9"
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    {
      "cell_type": "markdown",
      "source": [
        "# **📚Introduction:**"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "In the ever-evolving landscape of cinema, understanding audience sentiments towards movies is paramount. Movie reviews offer valuable insights into audience perceptions, but manually analyzing a large volume of reviews is time-consuming and often subjective. Enter Sentiment Analysis in Natural Language Processing (NLP), a powerful tool that automates the process of gauging sentiments from textual data. In this blog, we delve into the realm of movie reviews, exploring how Sentiment Analysis revolutionizes the way we perceive and analyze cinematic feedback."
      ],
      "metadata": {
        "id": "_Tp46-lAmC4_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Import necessary libraries**"
      ],
      "metadata": {
        "id": "Pzoo4Ok9mTvM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import re # for regex\n",
        "from nltk.corpus import stopwords\n",
        "# stopwords corpus within NLTK contains a collection of common words that are often considered irrelevant for analysis\n",
        "# and are thus typically removed from text data during preprocessing.\n",
        "from nltk.tokenize import word_tokenize #tokenize module, text data can be split into individual words or tokens,\n",
        "from nltk.stem import SnowballStemmer#stem module applies the Snowball stemming algorithm to reduce words to their root or base form\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pickle"
      ],
      "metadata": {
        "id": "d2bOybK3mhnG"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The CountVectorizer **from scikit-learn's feature extraction modu**le converts a collection of text documents into a matrix of token counts, representing the frequency of each word in the corpus, thereby enabling machine learning models to process textual data.\n",
        "\n",
        "\n",
        "**stopwords**corpus within NLTK contains a collection of common words that are often considered irrelevant for analysis and are thus typically removed from text data during preprocessing.\n",
        "\n",
        "The **pickle module** in Python provides functionality for serializing and deserializing Python objects, allowing for easy storage and retrieval of data structures, such as lists or dictionaries, in a binary format."
      ],
      "metadata": {
        "id": "S9JYhDBhoKi8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Load Dataset**"
      ],
      "metadata": {
        "id": "Zn_q_pJulyfL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kC6eKh_YmDNS",
        "outputId": "bbb0bbdc-f025-4fee-86a3-6e2bfaead209"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python code mounts the Google Drive to a Colab notebook, enabling access to files and directories stored on Google Drive within the notebook environment."
      ],
      "metadata": {
        "id": "V_9AiQ4_mafY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Courses /Data Science /NLP/Datasets/IMDB-Dataset.csv')\n",
        "print(data.shape)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "5brnbT6CmiOb",
        "outputId": "e3328fd6-d6f4-4305-dc88-b0adb4a71cd4"
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            "(50000, 2)\n"
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              "                                              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}"
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        "id": "s6HESfO5nEES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The info() method likely provides information about the data object, such as the data types of each column, memory usage, and non-null counts, commonly used in Python libraries like pandas for DataFrame objects."
      ],
      "metadata": {
        "id": "mF_NmdBanRX5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.sentiment.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8S6lu1tXnJnq",
        "outputId": "8ea861ce-9b12-4619-9f90-5aedb6d72b08"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sentiment\n",
              "positive    25000\n",
              "negative    25000\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This command returns the frequency count of different sentiment categories present in the 'data' object, aiding in the analysis of sentiment distribution within the dataset."
      ],
      "metadata": {
        "id": "KRl8w3XVnd4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.sentiment.replace('positive',1,inplace=True)\n",
        "data.sentiment.replace('negative',0,inplace=True)\n",
        "data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "EjSocUavnmnR",
        "outputId": "f9ad73c5-3db8-4924-fce5-d4bc4b4710ff"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                              review  sentiment\n",
              "0  One of the other reviewers has mentioned that ...          1\n",
              "1  A wonderful little production. <br /><br />The...          1\n",
              "2  I thought this was a wonderful way to spend ti...          1\n",
              "3  Basically there's a family where a little boy ...          0\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...          1\n",
              "5  Probably my all-time favorite movie, a story o...          1\n",
              "6  I sure would like to see a resurrection of a u...          1\n",
              "7  This show was an amazing, fresh & innovative i...          0\n",
              "8  Encouraged by the positive comments about this...          0\n",
              "9  If you like original gut wrenching laughter yo...          1"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "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\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This preprocessing step is called \"Label Encoding,\" where the categorical sentiment labels, such as 'positive', are replaced with numerical values, such as '1', in the 'data' object, and the changes are made in place."
      ],
      "metadata": {
        "id": "JUS4QjYEombS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Pre-processing Steps**"
      ],
      "metadata": {
        "id": "c0LicY-Dosig"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Remove HTML tags\n",
        "*   Remove special characters\n",
        "*   Convert everything to lowercase\n",
        "*   Remove stopwords\n",
        "*   Stemming"
      ],
      "metadata": {
        "id": "1eLK1QH5o2Ih"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1- Remove HTML tags**"
      ],
      "metadata": {
        "id": "PdpXsXRXpIh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def clean(text):\n",
        "    cleaned = re.compile(r'<.*?>')\n",
        "    return re.sub(cleaned,'',text)"
      ],
      "metadata": {
        "id": "6Pihtub-pQDD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.review = data.review.apply(clean)\n",
        "data.review[0]"
      ],
      "metadata": {
        "id": "ZRAUeMldpTAA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python function clean utilizes regular expressions to remove HTML tags from the input text and returns the cleaned text."
      ],
      "metadata": {
        "id": "fyXuGEZspin_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Remove special characters**"
      ],
      "metadata": {
        "id": "SOBNOy5ip8i5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def is_special(text):\n",
        "    rem = ''\n",
        "    for i in text:\n",
        "        if i.isalnum():\n",
        "            rem = rem + i\n",
        "        else:\n",
        "            rem = rem + ' '\n",
        "    return rem"
      ],
      "metadata": {
        "id": "u6DhDLMzqCAE"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.review = data.review.apply(is_special)\n",
        "data.review[0]"
      ],
      "metadata": {
        "id": "0kMRUP4IqFix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python function is_special removes non-alphanumeric characters from the input text and replaces them with whitespace, returning the modified text."
      ],
      "metadata": {
        "id": "agECFrEfqSzI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Convert everything to lowercase**"
      ],
      "metadata": {
        "id": "7e8FJehS1PnN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def to_lower(text):\n",
        "    return text.lower()"
      ],
      "metadata": {
        "id": "7gdj0W9a1Uw8"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.review = data.review.apply(to_lower)\n",
        "data.review[0]"
      ],
      "metadata": {
        "id": "n1uTWF8l1YIR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Remove stopwords**"
      ],
      "metadata": {
        "id": "6ZhRDj9N1dHs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-se7_qf1hZE",
        "outputId": "6bdd0e17-088e-4823-b2a1-aee0f7728c54"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python code imports the Natural Language Toolkit (NLTK) library and downloads the stopwords corpus, which contains common words like \"the,\" \"is,\" and \"and,\" used in text processing tasks for filtering out irrelevant words."
      ],
      "metadata": {
        "id": "Y0LlKQFD1tc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkncE9Rd1ybj",
        "outputId": "4b932bec-6189-4410-eb42-094ae70736e9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python code imports the Natural Language Toolkit (NLTK) library and downloads the Punkt tokenizer models, which are used for tokenization tasks like splitting text into individual words or sentences."
      ],
      "metadata": {
        "id": "929kBBWW1_Bq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rem_stopwords(text):\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    words = word_tokenize(text)\n",
        "    return [w for w in words if w not in stop_words]"
      ],
      "metadata": {
        "id": "SxhnPK5t2Hgb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.review = data.review.apply(rem_stopwords)\n",
        "data.review[0]"
      ],
      "metadata": {
        "id": "_32SfR0E2K1Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python function rem_stopwords removes stopwords from the input text using NLTK's English stopwords corpus and tokenizes the text into words, returning a list of words excluding the stopwords."
      ],
      "metadata": {
        "id": "XkPRHjdS2diT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5-Stem the words**"
      ],
      "metadata": {
        "id": "HTUsWUdv2hSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def stem_txt(text):\n",
        "    ss = SnowballStemmer('english')\n",
        "    return \" \".join([ss.stem(w) for w in text])"
      ],
      "metadata": {
        "id": "g-r3IyeM2lWG"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.review = data.review.apply(stem_txt)\n",
        "data.review[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "xWjbLZ2r2n-D",
        "outputId": "416610c9-0e58-44c1-f91c-43448fcc4c61"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one review mention watch 1 oz episod hook right exact happen first thing struck oz brutal unflinch scene violenc set right word go trust show faint heart timid show pull punch regard drug sex violenc hardcor classic use word call oz nicknam given oswald maximum secur state penitentari focus main emerald citi experiment section prison cell glass front face inward privaci high agenda em citi home mani aryan muslim gangsta latino christian italian irish scuffl death stare dodgi deal shadi agreement never far away would say main appeal show due fact goe show dare forget pretti pictur paint mainstream audienc forget charm forget romanc oz mess around first episod ever saw struck nasti surreal say readi watch develop tast oz got accustom high level graphic violenc violenc injustic crook guard sold nickel inmat kill order get away well manner middl class inmat turn prison bitch due lack street skill prison experi watch oz may becom comfort uncomfort view that get touch darker side'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Python function stem_txt stems the words in the input text using the Snowball Stemmer for English and returns a string where each word is replaced with its stem."
      ],
      "metadata": {
        "id": "ghpVk5zw22qz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "id": "-pWxnBfu2-hp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📚Model Training"
      ],
      "metadata": {
        "id": "B8rliopO3AOz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Creating Bag Of Words (BOW)**"
      ],
      "metadata": {
        "id": "SB4_bo0f3Tmm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(data.iloc[:,0].values)\n",
        "y = np.array(data.sentiment.values)\n",
        "cv = CountVectorizer(max_features = 1000)\n",
        "X = cv.fit_transform(data.review).toarray()\n",
        "print(\"X.shape = \",X.shape)\n",
        "print(\"y.shape = \",y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoSUjiNW3YmZ",
        "outputId": "bc129e76-6423-4bbf-af79-ebdfbc5b31b0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X.shape =  (50000, 1000)\n",
            "y.shape =  (50000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "This Python code segment converts the 'review' column of the 'data' object into a bag-of-words representation using CountVectorizer with a maximum of 1000 features, assigns it to 'X', and extracts the 'sentiment' column into 'y', then prints the shapes of 'X' and 'y'."
      ],
      "metadata": {
        "id": "928iccwA3jOK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a Bag of Words (BOW) involves representing text data as a collection of unique words and their frequencies, disregarding grammar and word order, essentially converting text into numerical vectors for machine learning tasks."
      ],
      "metadata": {
        "id": "5aHl-qCg35bg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Train test split**"
      ],
      "metadata": {
        "id": "2hK5WR3J4BaI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainx,testx,trainy,testy = train_test_split(X,y,test_size=0.2,random_state=9)\n",
        "print(\"Train shapes : X = {}, y = {}\".format(trainx.shape,trainy.shape))\n",
        "print(\"Test shapes : X = {}, y = {}\".format(testx.shape,testy.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUJiG8do4FVn",
        "outputId": "2c104cf4-dda2-4c0e-9db1-e10bfc7f18d9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train shapes : X = (40000, 1000), y = (40000,)\n",
            "Test shapes : X = (10000, 1000), y = (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Defining the models and Training them**"
      ],
      "metadata": {
        "id": "jTKoC6tJ4O5R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gnb,mnb,bnb = GaussianNB(),MultinomialNB(alpha=1.0,fit_prior=True),BernoulliNB(alpha=1.0,fit_prior=True)\n",
        "gnb.fit(trainx,trainy)\n",
        "mnb.fit(trainx,trainy)\n",
        "bnb.fit(trainx,trainy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ENpD1unt4S-p",
        "outputId": "8dc977ae-b07b-4ebb-af74-82605465f800"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BernoulliNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Model Testing**"
      ],
      "metadata": {
        "id": "ToEjW0wJ4ch2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Prediction and accuracy metrics to choose best model**"
      ],
      "metadata": {
        "id": "GH4QqXlF4rhp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ypg = gnb.predict(testx)\n",
        "ypm = mnb.predict(testx)\n",
        "ypb = bnb.predict(testx)\n"
      ],
      "metadata": {
        "id": "qbVftzXd4vht"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Gaussian = \",accuracy_score(testy,ypg))\n",
        "print(\"Multinomial = \",accuracy_score(testy,ypm))\n",
        "print(\"Bernoulli = \",accuracy_score(testy,ypb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsALoMD14zmq",
        "outputId": "79b83e2f-c223-4538-ac3e-d480584632d8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gaussian =  0.7843\n",
            "Multinomial =  0.831\n",
            "Bernoulli =  0.8386\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Model saving**"
      ],
      "metadata": {
        "id": "evT3jdZO42OI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(bnb,open('model1.pkl','wb'))"
      ],
      "metadata": {
        "id": "wFN7ruFP5H6J"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Performance Checking**"
      ],
      "metadata": {
        "id": "cJ46QUQL5Vqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rev =  \"\"\"Terrible. Complete trash. Brainless tripe. Insulting to anyone who isn't an 8 year old fan boy. Im actually pretty disgusted that this movie is making the money it is - what does it say about the people who brainlessly hand over the hard earned cash to be 'entertained' in this fashion and then come here to leave a positive 8.8 review?? Oh yes, they are morons. Its the only sensible conclusion to draw. How anyone can rate this movie amongst the pantheon of great titles is beyond me.\n",
        "\n",
        "So trying to find something constructive to say about this title is hard...I enjoyed Iron Man? Tony Stark is an inspirational character in his own movies but here he is a pale shadow of that...About the only 'hook' this movie had into me was wondering when and if Iron Man would knock Captain America out...Oh how I wished he had :( What were these other characters anyways? Useless, bickering idiots who really couldn't organise happy times in a brewery. The film was a chaotic mish mash of action elements and failed 'set pieces'...\n",
        "\n",
        "I found the villain to be quite amusing.\n",
        "\n",
        "And now I give up. This movie is not robbing any more of my time but I felt I ought to contribute to restoring the obvious fake rating and reviews this movie has been getting on IMDb.\"\"\"\n"
      ],
      "metadata": {
        "id": "xsaeGaK45mst"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1 = clean(rev)\n",
        "f2 = is_special(f1)\n",
        "f3 = to_lower(f2)\n",
        "f4 = rem_stopwords(f3)\n",
        "f5 = stem_txt(f4)"
      ],
      "metadata": {
        "id": "pearib-w5sRq"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bow,words = [],word_tokenize(f5)\n",
        "for word in words:\n",
        "    bow.append(words.count(word))\n",
        "#np.array(bow).reshape(1,3000)\n",
        "#bow.shape\n",
        "word_dict = cv.vocabulary_\n",
        "pickle.dump(word_dict,open('bow.pkl','wb'))"
      ],
      "metadata": {
        "id": "vrhejz1G53bw"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inp = []\n",
        "for i in word_dict:\n",
        "    inp.append(f5.count(i[0]))\n",
        "y_pred = bnb.predict(np.array(inp).reshape(1,1000))"
      ],
      "metadata": {
        "id": "qYY4HRh558Pd"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred)"
      ],
      "metadata": {
        "id": "eKSkUraK6AMh",
        "outputId": "e3e1292a-f13d-4e05-cb4b-bbe144bef723",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    }
  ]
}