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numpy scikit-learn nltk matplotlib seaborn transformers datasets torch shap" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LQvPTG-SWwmx", "outputId": "243497b4-a0fe-4dad-e793-b3c38aed5b83" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n", "Requirement already satisfied: nltk in /usr/local/lib/python3.12/dist-packages (3.9.1)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n", "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.0.0)\n", "Requirement 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(0.0.4)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (4.0.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (2.19.2)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.1.2)\n" ] } ] }, { "cell_type": "markdown", "source": [ "2 **Import** **Libraries**" ], "metadata": { "id": "v1fhM50SXIEo" } }, { "cell_type": "code", "source": [ "# Data manipulation\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Visualization\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Dataset loader\n", "from datasets import load_dataset\n", "\n", "# Text preprocessing\n", "import nltk\n", "import re\n", "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer\n", "\n", "# Machine learning tools\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", "from sklearn.metrics import classification_report, confusion_matrix\n", "\n", "# Transformer libraries\n", "import torch\n", "from transformers import AutoTokenizer\n", "from transformers import AutoModelForSequenceClassification\n", "from transformers import TrainingArguments, Trainer\n", "\n", "# Model interpretability\n", "import shap" ], "metadata": { "id": "jsFCb16YXKmT" }, "execution_count": 11, "outputs": [] }, { "cell_type": "markdown", "source": [ "3. Check GPU Availability" ], "metadata": { "id": "YTYRax2VXW4_" } }, { "cell_type": "code", "source": [ "# Check GPU availability\n", "import torch\n", "\n", "print(\"GPU Available:\", torch.cuda.is_available())\n", "\n", "if torch.cuda.is_available():\n", " print(\"GPU Name:\", torch.cuda.get_device_name(0))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "11p-u5BgXYTd", "outputId": "fbfc9ead-1638-497c-c1da-ebc1f0f542eb" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU Available: True\n", "GPU Name: Tesla T4\n" ] } ] }, { "cell_type": "markdown", "source": [ "**4. Load IMDB Dataset**" ], "metadata": { "id": "21OmS1cOXbiU" } }, { "cell_type": "code", "source": [ "# Load IMDB sentiment dataset\n", "dataset = load_dataset(\"imdb\")\n", "\n", "# Convert dataset to pandas DataFrame\n", "train_df = pd.DataFrame(dataset[\"train\"])\n", "test_df = pd.DataFrame(dataset[\"test\"])\n", "\n", "# Display sample data\n", "train_df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 673, "referenced_widgets": [ "ac7985eea8ba4e1f9e866f1c2083d69a", "0813ea5ea31f4e0685423da4b49f7331", "4f1f8a18799d403ea7ce8b289837e19b", "9412741d6b304e2e9ac386eda5978d7e", "0104e25b8f634ad9b185adc16437a72e", "1fa7b06c44ad4db88b46445ab1e2813c", "7cd3a7ff6c444e71b77f1617d90e8bde", "2cf992af5a154f68a474f143c94690ee", "2b059a035d99482b98aa44eede124267", "9624888ac4554cfc9cf6f69679195db5", "47af5a33e59041a09a1d046d50f71640", "cb61d3014dcc4d4fa34a26c86489ed9f", "0f8757ac756a451a986408d52702bee7", "44c50e3c29734a3592a711528c9be3c4", "f3fd0db356fe4507b32b7530289c9978", "383e5ef6316f423995311a3c948eec2c", "0b5684ec63c34f4fbd0da297d8b5c4ce", "57fae60c8c9a442995f30a97c21b001e", "e531b5a437894e46b9e1b7535f2081bc", "20fdc1fd82c24a3ab91e584fc6fa4597", "5885764cbf8f41289b5ebfb8177f110d", "8eefe07e198c4ae8a35a90797d36878d", "7546f0fb3dea44e7b59a3229215717f3", "38c50a655cc245f888d8ed73c9ddf190", "1817dba97ebd427a84066bc9be2e9e2e", "430d8742675e4ff282e83a7584f91f44", "7e97264e3acf4eb78a23f8ee123100ae", "9479abb1d8f246008e014b2182a22bf5", "a510fca7da26420487cce6b6d8dec31a", "fb371e9e76ff4566a456c28b8e08ee0a", "60f454fa4fc445c897ff859997d30d96", "0bd46943562e46939545fa5e58003831", "5fa8946eb7514678bf1c0ffa4777ac4a", "018fb5b4aa5e41b0aa1225eba1fcbc7d", "bfb6c844200e44b8ae255ffc1e1343ca", "d999c0cf582144db84073739a780299f", "742128eb7aa145c48743b50f04e7a2c1", "c6bd150efc884aa8ab9d3526cac66a43", "e0b264d75c5941d284ca82ab02d9ab3c", "7baec6263ca549eaa22952827cfbd7d2", "af2bed1a723a4b0393cbe82418360c1a", "3fdebcab43cd4aba9f2ff4223152c1eb", "b8595e9dcaff4d95b38e0c9a31c0d341", "271dc3bdf1f64c3fa729650914d32b0f", "3b5ce231eb8e4c0e956661c138b8a91e", "1eb64f125c2247e68f2b9d9fb6485daf", "480902a7964f4400a66505ab71fbb956", "42e8044df7a440fe90b70e8bc1551efa", "8c39d1c68e094a2e865ecf3b569965b1", "928c9a63df6c49daad7cc169434d41a6", "46fb0cd809654f2cb8b97bd5dcb6217d", "5259d18f58804f1fa648bd0d1689e27a", "0f17885408db4f40af15c659a020335e", "993c11791c0445c1a06d68fa4ed59b58", "95621c526f064278b0407cf91e1de56e", "ce34fe63481d4224934bb24ba20a8352", "6633e2728b604f03877baadf5c081662", "b479f1cf23304a37931f71e3a03ad354", "fcb223c5391f4c2bb59e2d6b0ff1c0e7", "47ebb563bfb544dc90fd9a52c8de36d9", "4021e672603d4414ab533cb27c7598e1", "8b227acc738e4f16a25e299c78924dbb", "21ee0047fc894b2f89ea064bbb7a0f96", "a8a6ff3f8b0d4601852ad62efa04bb8a", "65a6e3c2551b4e4d88e57b82f643a532", "c89f6258d8874e15a52678205d8226d5", "9ccf9b954e9f4a5d9e52a70ff4c34b7c", "3aea263f65544e4e8939b05c3e5a8be0", "db95dea79d15481cbf238f03a6ef31e5", "11074c61148b492b9b7a5cdd8943c219", "769227749dd8419f8b7cb5a22b099418", "863a620042d344db9bf83f015fc3bb65", "d243d181a06442e593e013b43b7a0bc6", "59402436e1304493bce62e5764ed0ba2", "c6377a9c38ac4dd9ae82068364d59e96", "2f46cd84e33f49e7b0686abf710a164f", "31c053ce6d6e472d916266aeb3a8dbf2" ] }, "id": "hK5kDSccXiEH", "outputId": "70f3576c-e636-4a26-a5b9-6be186e882bd" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "ac7985eea8ba4e1f9e866f1c2083d69a" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "plain_text/train-00000-of-00001.parquet: 0%| | 0.00/21.0M [00:00\n", "
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textlabel
0I rented I AM CURIOUS-YELLOW from my video sto...0
1\"I Am Curious: Yellow\" is a risible and preten...0
2If only to avoid making this type of film in t...0
3This film was probably inspired by Godard's Ma...0
4Oh, brother...after hearing about this ridicul...0
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train_df", "summary": "{\n \"name\": \"train_df\",\n \"rows\": 25000,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 24904,\n \"samples\": [\n \"Although I didn't like Stanley & Iris tremendously as a film, I did admire the acting. Jane Fonda and Robert De Niro are great in this movie. I haven't always been a fan of Fonda's work but here she is delicate and strong at the same time. De Niro has the ability to make every role he portrays into acting gold. He gives a great performance in this film and there is a great scene where he has to take his father to a home for elderly people because he can't care for him anymore that will break your heart. I wouldn't really recommend this film as a great cinematic entertainment, but I will say you won't see much bette acting anywhere.\",\n \"Yet again one of the most misunderstood Goddesses of my country has been twisted by \\\"Westerners\\\" who cannot understand the esoteric symbolism of the Mother Goddess in her dark forms. The Mother takes on the frightening form of Kali Mata to destroy our inner demons, and to terrify our egos. And though blood sacrifice is given to Kali and Durga, the events depicted in this film are just absurd. The Mother takes on a wrathful form to be wrathful to our inner demons, limitations, and ego when no other form will suffice. It's also in her wrathful form that she burns away all your Karmas in the \\\"Smashan\\\" fires that you cultivate in your heart for her to dance on if you love her, and she will bring you to reality and truth. Reality and truth has a dark side as well as light, which serves a purpose. The Mother is the embodiment of the physical Universe as well, she is Nature. Nature can be cruel and destructive to maintain balance. You cannot have growth and life without death and destruction. Kali represents the force of destruction for the purpose of new growth and life both mundane and spiritual in the universe. It's very outrageous to me that people who know nothing of India or it's divinity can just take one of our beloved Goddesses and use her like a cheap prostitute to make some low-budget, talentless horror film. How dare they take our beloved Mother and portray her as a horror that makes people chop their eyelids off!? She is only horrific to those who are attached to their ego and who live in delusion , greed, anger, and other inner-demons. It's very clear to me that the person who wrote this movie must have a very serious self-deluding ego, and serious inner-demons to see Kali as so horrible and terrible. When the ego drops away she becomes a form that is enchanting, beautiful, and young, a beauty that is so enchanting to behold that she enchants the entire Universe with it. Kali Maa is an ancient Mother, not to be trifled with for the sake of entertainment, let's just hope that in her endless compassion and mercy that she does not take on wrathful form to those involved with this movie.

The audacity that Westerners have in using religions like my own, or the religions of the Caribbean Islands such as Santeria, and Vodou which are actually very positive, and other such religions to twist and exaggerate misunderstood elements that the Western mind cannot comprehend, is totally ridiculous. It's clear that there is no respect for what people live, breathe and believe in when it comes to these kind of flicks.

Kali Maa in reality is a caring and compassionate mother, whom we shed tears at her beautiful feet in devotion and love for. And I am happy that my Mother takes on wrathful form sometimes to protect her devotees from themselves and from outside forces.

Many Praises to the REAL Kali Maa, who has shown MANY the path of God and realization.\",\n \"Paris is the place to be to enjoy beautiful art and music, and to fall madly in love - as is the case in this film. Boy meets girl, they fall in love, but something stands in their way of eternal happiness, the classic story.

The wonderful music of George Gerschwin complements the great dancing by Gene Kelly and Leslie Caron. \\\"An American in Paris\\\" is a humorous, light-hearted, loving film well worth watching.

8/10

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "markdown", "source": [ "Dataset Information" ], "metadata": { "id": "ks0br5f0Xnbo" } }, { "cell_type": "code", "source": [ "print(\"Training samples:\", len(train_df))\n", "print(\"Testing samples:\", len(test_df))\n", "\n", "print(\"\\nClass distribution:\")\n", "print(train_df[\"label\"].value_counts())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RLT4hKghXyrT", "outputId": "788659d7-53d5-4297-dfcc-f2bda05ededb" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training samples: 25000\n", "Testing samples: 25000\n", "\n", "Class distribution:\n", "label\n", "0 12500\n", "1 12500\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "markdown", "source": [ "Label Meaning\n", "\n", "0 = Negative sentiment\n", "\n", "1 = Positive sentiment" ], "metadata": { "id": "sYWkTZYhYBns" } }, { "cell_type": "markdown", "source": [ "**5. Exploratory Data Analysis (EDA)**\n", "\n", "**Sentiment Class Distribution**\n" ], "metadata": { "id": "spw8wkgmX7IX" } }, { "cell_type": "code", "source": [ "plt.figure(figsize=(6,4))\n", "\n", "sns.countplot(x=train_df[\"label\"])\n", "\n", "plt.title(\"Sentiment Class Distribution\")\n", "plt.xlabel(\"Sentiment (0=Negative, 1=Positive)\")\n", "plt.ylabel(\"Number of Reviews\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 410 }, "id": "C7mvMMSBX5sy", "outputId": "12a86319-1168-4272-d666-59f3c0f43a51" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Review Length Analysis**" ], "metadata": { "id": "LrjQDynpYH2d" } }, { "cell_type": "code", "source": [ "train_df[\"review_length\"] = train_df[\"text\"].apply(len)\n", "\n", "plt.figure(figsize=(8,5))\n", "\n", "sns.histplot(train_df[\"review_length\"], bins=50)\n", "\n", "plt.title(\"Distribution of Review Lengths\")\n", "plt.xlabel(\"Review Length\")\n", "plt.ylabel(\"Frequency\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 474 }, "id": "j2rjIkt3YMA4", "outputId": "9d7a8045-3c45-4c3c-b448-fdcb3d7ada1a" }, "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**6. Text Preprocessing**\n", "\n", "\n", "Download NLP resources" ], "metadata": { "id": "a_h19ovKYUPX" } }, { "cell_type": "code", "source": [ "nltk.download(\"stopwords\")\n", "nltk.download(\"wordnet\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vn2vcClpYagc", "outputId": "3d8e3ec0-7c47-4cba-c2f8-9c50bf3995f2" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "markdown", "source": [ "Define preprocessing function" ], "metadata": { "id": "p--apNexYkKf" } }, { "cell_type": "code", "source": [ "stop_words = set(stopwords.words(\"english\"))\n", "lemmatizer = WordNetLemmatizer()\n", "\n", "def preprocess_text(text):\n", "\n", " # Convert text to lowercase\n", " text = text.lower()\n", "\n", " # Remove special characters and numbers\n", " text = re.sub(r\"[^a-zA-Z]\", \" \", text)\n", "\n", " # Tokenization\n", " words = text.split()\n", "\n", " # Remove stopwords\n", " words = [word for word in words if word not in stop_words]\n", "\n", " # Lemmatization\n", " words = [lemmatizer.lemmatize(word) for word in words]\n", "\n", " # Join words back into sentence\n", " return \" \".join(words)" ], "metadata": { "id": "-tpNTm9QYkkZ" }, "execution_count": 18, "outputs": [] }, { "cell_type": "markdown", "source": [ "Apply preprocessing" ], "metadata": { "id": "a7pfAVLlYwWm" } }, { "cell_type": "code", "source": [ "train_df[\"clean_text\"] = train_df[\"text\"].apply(preprocess_text)\n", "test_df[\"clean_text\"] = test_df[\"text\"].apply(preprocess_text)" ], "metadata": { "id": "avz31Jx1YxLj" }, "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "source": [ "Preview cleaned text" ], "metadata": { "id": "-Glld5Q8Y8AX" } }, { "cell_type": "code", "source": [ "train_df[[\"text\",\"clean_text\"]].head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 293 }, "id": "n_tmPp6PZD1T", "outputId": "7c38a0cd-f3f9-4ede-ccbf-ea931d4ae138" }, "execution_count": 20, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text \\\n", "0 I rented I AM CURIOUS-YELLOW from my video sto... \n", "1 \"I Am Curious: Yellow\" is a risible and preten... \n", "2 If only to avoid making this type of film in t... \n", "3 This film was probably inspired by Godard's Ma... \n", "4 Oh, brother...after hearing about this ridicul... \n", "\n", " clean_text \n", "0 rented curious yellow video store controversy ... \n", "1 curious yellow risible pretentious steaming pi... \n", "2 avoid making type film future film interesting... \n", "3 film probably inspired godard masculin f minin... \n", "4 oh brother hearing ridiculous film umpteen yea... 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0I rented I AM CURIOUS-YELLOW from my video sto...rented curious yellow video store controversy ...
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"train_df[[\\\"text\\\",\\\"clean_text\\\"]]\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"\\\"I Am Curious: Yellow\\\" is a risible and pretentious steaming pile. It doesn't matter what one's political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn't true. I've seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don't exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we're treated to the site of Vincent Gallo's throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) \\\"double-standard\\\" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won't see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women's bodies.\",\n \"Oh, brother...after hearing about this ridiculous film for umpteen years all I can think of is that old Peggy Lee song..

\\\"Is that all there is??\\\" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into \\\"Goodbye Columbus\\\"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!

The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.

Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!

Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren't for the censorship scandal, it would have been ignored, then forgotten.

Instead, the \\\"I Am Blank, Blank\\\" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that \\\"naughty sex film\\\" that \\\"revolutionized the film industry\\\"...

Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the \\\"dirty\\\" parts, just to get it over with.

\",\n \"If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.

One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).

One might better spend one's time staring out a window at a tree growing.

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"clean_text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"curious yellow risible pretentious steaming pile matter one political view film hardly taken seriously level claim frontal male nudity automatic nc true seen r rated film male nudity granted offer fleeting view r rated film gaping vulva flapping labium nowhere exist go crappy cable show schlongs swinging breeze clitoris sight pretentious indie movie like brown bunny treated site vincent gallo throbbing johnson trace pink visible chloe sevigny cry implying double standard matter nudity mentally obtuse take account one unavoidably obvious anatomical difference men woman genitals display actress appears nude cannot said man fact generally see female genitals american film anything short porn explicit erotica alleged double standard less double standard admittedly depressing ability come term culturally inside woman body\",\n \"oh brother hearing ridiculous film umpteen year think old peggy lee song br br early teen smoked fish hit u young get theater although manage sneak goodbye columbus screening local film museum beckoned finally could see film except old parent schlepped see br br reason film condemned anonymous sand time obscenity case sparked u release million people flocked stinker thinking going see sex film instead got lot closeup gnarly repulsive swede street interview bland shopping mall asinie political pretension feeble care simulated sex scene saggy pale actor br br cultural icon holy grail historic artifact whatever thing shred burn stuff ash lead box br br elite esthete still scrape find value boring pseudo revolutionary political spewings censorship scandal would ignored forgotten br br instead blank blank rhythymed title repeated endlessly year titilation porno film curious lavender gay film curious black blaxploitation film etc every ten year thing rise dead viewed new generation sucker want see naughty sex film revolutionized film industry br br yeesh avoid like plague must see rent video fast forward dirty part get br br\",\n \"avoid making type film future film interesting experiment tell cogent story br br one might feel virtuous sitting thru touch many important issue without discernable motive viewer come away new perspective unless one come one one mind wanders invariably pointless film br br one might better spend one time staring window tree growing br br\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "markdown", "source": [ "**7. Traditional Machine Learning Model**\n", "\n", "**TF-IDF + Logistic Regression **\n", "\n", "Convert text to numerical features" ], "metadata": { "id": "dacvEId_ZJhd" } }, { "cell_type": "code", "source": [ "vectorizer = TfidfVectorizer(max_features=10000)\n", "\n", "X_train = vectorizer.fit_transform(train_df[\"clean_text\"])\n", "X_test = vectorizer.transform(test_df[\"clean_text\"])\n", "\n", "y_train = train_df[\"label\"]\n", "y_test = test_df[\"label\"]" ], "metadata": { "id": "mmD09mHpZKsl" }, "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Train Logistic Regression Model**" ], "metadata": { "id": "pf--SCQaZU6o" } }, { "cell_type": "code", "source": [ "lr_model = LogisticRegression(max_iter=200)\n", "\n", "lr_model.fit(X_train, y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 80 }, "id": "kWv3IVmjZWQc", "outputId": "257ecb99-899e-4af4-b1dc-472cd18f37ac" }, "execution_count": 22, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LogisticRegression(max_iter=200)" ], "text/html": [ "
LogisticRegression(max_iter=200)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "markdown", "source": [ "**Predict Sentiment**" ], "metadata": { "id": "DUfd1DQsZfHb" } }, { "cell_type": "code", "source": [ "y_pred = lr_model.predict(X_test)" ], "metadata": { "id": "VRn2DRmgZgA7" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Model Evaluation**" ], "metadata": { "id": "nCsI5-2cZj5Y" } }, { "cell_type": "code", "source": [ "accuracy = accuracy_score(y_test, y_pred)\n", "precision = precision_score(y_test, y_pred)\n", "recall = recall_score(y_test, y_pred)\n", "f1 = f1_score(y_test, y_pred)\n", "\n", "print(\"Accuracy:\", accuracy)\n", "print(\"Precision:\", precision)\n", "print(\"Recall:\", recall)\n", "print(\"F1 Score:\", f1)\n", "\n", "print(\"\\nClassification Report:\\n\")\n", "print(classification_report(y_test, y_pred))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yyTc5NszZouO", "outputId": "667ab5b3-c0ed-49f0-fb26-c38a4ed385a9" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 0.87904\n", "Precision: 0.8785554490252477\n", "Recall: 0.87968\n", "F1 Score: 0.8791173648864726\n", "\n", "Classification Report:\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.88 0.88 0.88 12500\n", " 1 0.88 0.88 0.88 12500\n", "\n", " accuracy 0.88 25000\n", " macro avg 0.88 0.88 0.88 25000\n", "weighted avg 0.88 0.88 0.88 25000\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Confusion Matrix**" ], "metadata": { "id": "eSA159JKZtBi" } }, { "cell_type": "code", "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "\n", "plt.figure(figsize=(6,4))\n", "\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n", "\n", "plt.xlabel(\"Predicted Label\")\n", "plt.ylabel(\"True Label\")\n", "plt.title(\"Confusion Matrix – Logistic Regression\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 410 }, "id": "BmHZcCYWZxgQ", "outputId": "b69a224d-460c-47de-d7d5-2bbca1f7c0ce" }, "execution_count": 25, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**8. Transformer Model (DistilBERT)**\n", "\n", "\n", "DistilBERT is a lighter and faster version of BERT suitable for academic experiments." ], "metadata": { "id": "soPxupo0Z2oC" } }, { "cell_type": "markdown", "source": [ "**Load Tokenizer**" ], "metadata": { "id": "LxDyUGZEaBhR" } }, { "cell_type": "code", "source": [ "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "8585df48edbf43e7b93a9374d2a775c7", "6a51f047c2bc4e8f87b453c8fa591209", "f985d41025fe427b9e30621504103c43", "2ab9e9cb3dce4c19b028dcde249ef0b6", "d5721aa01b614643876ad500979f39d0", "69aee268f1ec4901b7c5d567f43a8a41", "4c752b0943ab40b6890579ea671c6c86", "0ec27ffe58cf472aa9dfecf7011117cc", "7a5714b5672d47dda6a49a48e7e2ac3a", "5cc0e738d34142e7a55bb1a11ce584a9", "304ffd5abf1f41b0bc9e56bb79ad7f72", "e425187cee9549b28f3494e62d1908c2", "f4f3794b92de45128e2e8c952edb473f", "39e2f06f6fd340a8850b2bb90f5a5f43", "bcade8419e2e44859802277457bbbc92", "3295ce60c51446b485d420c345b07dbb", "9c45715e107e42a2b5b422f59966851a", "b81f116ca2c54c2f82bf5f5b5d59f320", "9a441898f7c14b69a34e2ba67c1cd13c", "b017abf81df849499798933745bb7fa6", "575111166bbc42dc913b1102dca7024e", "81c9fe627dfa45f3a42ebc62832b13dd", "4d048f26c5284a41bd22076611a7dae4", "e5869f98c97a4e7fb0270c6b775aa276", "e1df6e466f4a4b49b562a8cdfa3d12a4", "21dcc3093421495eb1f416af3f430f06", "263308a38e8448ee98f48bc36707a79c", "02be2725815246eca5384997b1cdccf2", "f321ae517957446589e9878e38928fe5", "e7d7eb711e8d46c89a95327da3e0a66f", "ada02d06f1804828a51977273194c082", "54863f78a2574e118ea05ef5bb794af9", "a64a870a22c047d4930bc116736136ff", "fc742fafda924dd7bb95919763123029", "9d578f3feb5e4ff38ba2934234340dc2", "f2f512df4900452a881c1c460f1bcf80", "d0d422e4aea1413fa9dd45dbd79a5861", "9fd22c1e218942c0a3772904d02e331c", "ecf33b5375264953a8626c2f37d92753", "d4304e3b33cc40ad8da2194d3020bd30", "a94240cf9afd4d65b825d53fb2e2e854", "d24fb510ba2e43e8be2e3984043bc9ba", "82948459989f4d07a2424878cca39933", "136e872422b64500b0001f658bae49f9" ] }, "id": "4jaAYl8naGC7", "outputId": "e13f1280-acb4-439e-bcee-6f829a2a7f99" }, "execution_count": 26, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/483 [00:00" ], "text/html": [ "\n", "
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ModelAccuracy
0TF-IDF + Logistic Regression0.87904
1DistilBERT Transformer0.92200
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "comparison", "summary": "{\n \"name\": \"comparison\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"DistilBERT Transformer\",\n \"TF-IDF + Logistic Regression\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03037730731977408,\n \"min\": 0.87904,\n \"max\": 0.922,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.922,\n 0.87904\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "markdown", "source": [ "**10. Model Interpretability (SHAP)**\n", "\n", "\n", "SHAP helps explain which words influence sentiment predictions." ], "metadata": { "id": "p4bRBH8WeEwN" } }, { "cell_type": "code", "source": [ "explainer = shap.Explainer(lr_model, X_train)\n", "\n", "shap_values = explainer(X_test[:200])\n", "\n", "shap.plots.bar(shap_values)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 454 }, "id": "xZce42BqeGIH", "outputId": "eb82838e-827a-40b3-de14-f351343b37ab" }, "execution_count": 38, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**11. Example Sentiment Prediction**" ], "metadata": { "id": "A2Q47qR7eRR_" } }, { "cell_type": "code", "source": [ "sample_review = \"This movie was fantastic with amazing acting.\"\n", "\n", "clean_review = preprocess_text(sample_review)\n", "\n", "vector = vectorizer.transform([clean_review])\n", "\n", "prediction = lr_model.predict(vector)\n", "\n", "if prediction[0] == 1:\n", " print(\"Predicted Sentiment: Positive\")\n", "else:\n", " print(\"Predicted Sentiment: Negative\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR7wslK6eg5M", "outputId": "f2db5d77-f834-428a-a71a-22fcbc461931" }, "execution_count": 39, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Predicted Sentiment: Positive\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Hyperparameter Tuning for Logistic Regression\n", "\n", "This section performs hyperparameter tuning using GridSearchCV to improve the traditional TF‑IDF + Logistic Regression model.\n" ], "metadata": { "id": "C99HduCaetla" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV\n", "\n", "param_grid = {\n", " \"C\": [0.01, 0.1, 1, 10],\n", " \"solver\": [\"liblinear\", \"lbfgs\"]\n", "}\n", "\n", "grid = GridSearchCV(\n", " LogisticRegression(max_iter=500),\n", " param_grid,\n", " cv=3,\n", " scoring=\"f1\",\n", " n_jobs=-1\n", ")\n", "\n", "grid.fit(X_train, y_train)\n", "\n", "best_lr_model = grid.best_estimator_\n", "print(\"Best parameters:\", grid.best_params_)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4ghx_i1ge1h1", "outputId": "e6f0cc03-f2b8-4e9b-9299-e5a34e8a321e" }, "execution_count": 40, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Best parameters: {'C': 1, 'solver': 'liblinear'}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Feature Importance for TF‑IDF Model\n", "\n", "This identifies the most influential positive and negative words used by the logistic regression classifier.\n" ], "metadata": { "id": "peIub_lNfReL" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "\n", "feature_names = vectorizer.get_feature_names_out()\n", "coefficients = best_lr_model.coef_[0]\n", "\n", "top_positive = np.argsort(coefficients)[-20:]\n", "top_negative = np.argsort(coefficients)[:20]\n", "\n", "print(\"Top Positive Words:\")\n", "print([feature_names[i] for i in top_positive])\n", "\n", "print(\"\\nTop Negative Words:\")\n", "print([feature_names[i] for i in top_negative])\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nW7HevQpfZPz", "outputId": "7d798cf3-1d24-445e-d717-78149b2106cd" }, "execution_count": 41, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Top Positive Words:\n", "['also', 'enjoyable', 'fantastic', 'definitely', 'superb', 'brilliant', 'today', 'love', 'enjoyed', 'fun', 'highly', 'loved', 'well', 'amazing', 'favorite', 'wonderful', 'perfect', 'best', 'excellent', 'great']\n", "\n", "Top Negative Words:\n", "['worst', 'bad', 'awful', 'waste', 'boring', 'poor', 'nothing', 'worse', 'terrible', 'horrible', 'poorly', 'unfortunately', 'dull', 'disappointment', 'supposed', 'annoying', 'disappointing', 'ridiculous', 'fails', 'save']\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Confusion Matrix for Logistic Regression" ], "metadata": { "id": "G5lRm5wYfkYR" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", "\n", "y_pred_lr = best_lr_model.predict(X_test)\n", "\n", "cm_lr = confusion_matrix(y_test, y_pred_lr)\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm_lr)\n", "disp.plot()\n", "plt.title(\"Logistic Regression Confusion Matrix\")\n", "plt.show()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "Y_x5KxRmfxSK", "outputId": "478a062d-8cc9-4e6d-ef69-49cc88770c4d" }, "execution_count": 42, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Define Metrics for BERT Model\n", "\n", "HuggingFace Trainer requires a custom compute_metrics function to calculate accuracy, precision, recall, and F1.\n" ], "metadata": { "id": "O7ym0oAngBZB" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n", "\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " preds = logits.argmax(axis=1)\n", " # Add zero_division=0 to handle cases where a class might be absent in a batch\n", " precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average=\"binary\", zero_division=0)\n", " acc = accuracy_score(labels, preds)\n", " return {\n", " \"accuracy\": acc,\n", " \"precision\": precision,\n", " \"recall\": recall,\n", " \"f1\": f1\n", " }" ], "metadata": { "id": "BbtvJQYVgGc0" }, "execution_count": 43, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Confusion Matrix for BERT Model" ], "metadata": { "id": "KaH1__BfgNI7" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", "\n", "predictions = trainer.predict(test_subset)\n", "y_pred_bert = np.argmax(predictions.predictions, axis=1)\n", "y_true_bert = predictions.label_ids\n", "\n", "cm_bert = confusion_matrix(y_true_bert, y_pred_bert)\n", "\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm_bert)\n", "disp.plot()\n", "plt.title(\"BERT Confusion Matrix\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "Q1AswfvggQVW", "outputId": "04ed3dcb-f566-4f33-e970-78a40a7049a6" }, "execution_count": 36, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "{'eval_loss': 0.3815971314907074, 'eval_accuracy': 0.9105, 'eval_precision': 0.0, 'eval_recall': 0.0, 'eval_f1': 0.0, 'eval_runtime': 36.3598, 'eval_samples_per_second': 55.006, 'eval_steps_per_second': 3.438, 'epoch': 2.0}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Training Time Comparison\n", "\n", "This measures computational efficiency between the traditional and transformer models.\n" ], "metadata": { "id": "jPzdWX_AgWHr" } }, { "cell_type": "code", "source": [ "import time\n", "\n", "start = time.time()\n", "best_lr_model.fit(X_train, y_train)\n", "lr_time = time.time() - start\n", "\n", "start = time.time()\n", "trainer.train()\n", "bert_time = time.time() - start\n", "\n", "print(\"Logistic Regression training time:\", lr_time)\n", "print(\"BERT training time:\", bert_time)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 257, "referenced_widgets": [ "268ad33dfe7246668bcbbf69e3004f2b", "7bc98fd22b844bc8a5cd60af3e2b001a", "d6f8581c4ec74ae3bb4c396f59aa161e", "2c1a04d962ec4f99987e25ebdff4f228", "a6afd188a4db4ae987a4635e5301f535", "79b27ed732e449aaad84cfe784a69ffb", "14f5355cd79d4b1eaf8dfc1f0b7129fc", "7ab60a7a51c54c558f4f0ee5f9f27cbe", "4b552a56d4794f2aa19450eccd9dfc73", "4163e98fc91b4d8e9f562fef0f37b8be", "60757cc9e5fe41c391e3cfc9f236b9d0", "706a6a9f93b4400b92da5ebd9a58b50d", "06ed4956ffcd471cb94df032a7d5028f", "68ab911eef544c6f9e719712c6d0a3d0", "0d6a39c271924ba5b24756d2d7383b73", "b17d77a1652040f79bd0ea94bda573bd", "7f1ed26b014e48d4bd4f479f99cd9a8a", "55a606a6ea294a4f94637d711cc81a6f", "f8989cd760a34e578e739f10a0efd9ca", "a2fa810082f34016a2d0b9252e53ac8a", "671e974dc3444a339b054f020051fe7e", "10ae0d44c17f45d481081507fc8da883" ] }, "id": "7uels5JfggkS", "outputId": "281289ab-8e93-449c-f192-59553c04899d" }, "execution_count": 46, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [1250/1250 18:48, Epoch 2/2]\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.0959640.2179120.9500000.0000000.0000000.000000
20.0550080.4319620.9185000.0000000.0000000.000000

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