💬 Sentiment Analysis using SVM and TF-IDF

This project demonstrates how to perform sentiment classification on text data using Support Vector Machine (SVM) and TF-IDF vectorization. The model predicts whether a text expresses positive or negative sentiment based on learned patterns from a small sample movie review dataset.

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🚀 Features

• Converts raw text to numeric features using TF-IDF
• Trains an SVM classifier with a linear kernel (ideal for text classification)
• Evaluates performance using a classification report
• Extracts and plots the most influential positive and negative features
• Visualizes top coefficients contributing to sentiment prediction

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📝 Dataset

A synthetic list of short movie reviews with manually labeled sentiments:

• 1 = Positive sentiment
• 0 = Negative sentiment

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📊 Example Output

              precision    recall  f1-score   support

    Negative       1.00      0.75      0.86         4
    Positive       0.80      1.00      0.89         4

    accuracy                           0.88         8
   macro avg       0.90      0.88      0.88         8
weighted avg       0.90      0.88      0.88         8

And a bar chart visualizing top positive and negative words based on their SVM coefficients.

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🛠️ How to Run

1. Clone the repository:

git clone https://github.com/NoorNick/Text-Classification.git
cd Text-Classification

2. Install the required packages:

pip install -r requirements.txt

3. Launch the notebook:

jupyter notebook

Then open text_classification.ipynb and run all cells.

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📌 Techniques Used

• TF-IDF Vectorization for feature extraction

• Support Vector Machine (SVC) for classification

• Train-test split with sklearn

• Interpretability through feature coefficient analysis

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Train smarter, classify better. 🧠📊