Sentiment Classifier

A simple web application built with Naive-Bayes, nltk & Streamlit to predict the sentiment of text as Positive or Negative. Originally trained on movie reviews, but it can handle general text statements as well.

Features

• Text preprocessing using NLTK:
  • Lowercasing
  • Removing HTML tags and special characters
  • Stopword removal
  • Stemming with Porter Stemmer
• Sentiment prediction using a pre-trained Naive Bayes model
• Real-time predictions via a user-friendly Streamlit interface

Installation

1. Clone the repository:

   git clone https://github.com/Abh3shek/sentiment_analysis.git
   cd sentiment_classifier
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate     # Windows
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Ensure the Model/ contains:

   - vectorizer.joblib
   - model.joblib
   

5. Run: streamlit run app.py Open your browser at the URL provided (usually http://localhost:8501) and enter a text to get the sentiment prediction.