🎉 Sentiment Classifier App 💬

Welcome to the Sentiment Classifier App! This app takes in a review and predicts whether it's positive or negative using machine learning. 🚀

📂 Dataset

We used a dataset of 84,166 reviews to train our model. The reviews are classified as positive or negative based on their content and score.

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🔍 Features

• 🧠 Trained on Logistic Regression – Achieving 91.22% accuracy!
• 🏋️ TF-IDF Vectorizer – Converts text to numerical features.
• ⚡ Real-time Sentiment Prediction – Input your review and get instant feedback.

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🛠️ How It Works

1. Train the Model

python train_model.py

• Loads the dataset, preprocesses reviews, trains the classifier, and saves the model.

2. Use the Model

python app.py

• Launch the interactive app where you can input reviews and receive predictions.

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🚀 Getting Started

🧩 Prerequisites

• Python 3.x
• Pandas, Scikit-Learn, Joblib

🔧 Installation

1. Clone the repo:

   git clone https://github.com/your-username/sentiment-classifier.git

2. Install dependencies:

   pip install -r requirements.txt

3. Train the model:

   python train_model.py

4. Start the app:

   python app.py

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⚙️ File Structure

.
├── train_model.py      # Model training script
├── predict_model.py    # Prediction module
├── app.py              # User interface script
├── reviews.csv         # Dataset file (84,166 reviews)
├── sentiment_model.pkl # Trained model
├── vectorizer.pkl      # TF-IDF vectorizer
└── README.md           # This file 😎

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🤔 How to Use

1. Run the app with:

   python app.py

2. Enter a review, and the app will predict whether it's positive or negative!

Enter a review: "I love this app, it’s amazing!"
The review is: positive 👍

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🧠 Model Performance

Achieved 91.22% accuracy on the dataset with Logistic Regression!

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🌟 Contributing

Feel free to fork this repo and contribute! We welcome improvements and new features.

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📧 Contact

For any inquiries, reach out at:

• GitHub: Arman Kianian
• Email: Kianianarman1@gmail.com

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Give this repo a ⭐ if you find it helpful! 😊

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