Sentiment Analysis Project

Overview-

This project implements a text classification machine learning model that analyses the customer's sentiments based on their reviews about a restaurant. The dataset is preprocessed, cleaned, and used to train a Naive Bayes classifier to classify reviews as either Positive or Negative.

Project Structure-

This project is focused on building a sentiment analysis model for restaurant reviews. It consists of the following major components:

• model.py: Code to train the sentiment analysis model. It uses a dataset of restaurant reviews and saves the trained model (model.pkl) and the CountVectorizer (cv-model.pkl).
• app.py: Flask app that serves as the main application. It provides:
  • A web interface to input reviews and get predictions.
  • A REST API to send reviews programmatically and get responses.
• request.py: A script to demonstrate how to call the Flask API using Python's requests library.
• templates/: Contains the HTML template for the web interface.
• static/: Contains static files like JavaScript and CSS.

How to Clone and Run the Project-

1. Clone the Repository:

   git clone https://github.com/sanskriti49/Sentiment-Analysis.git
   cd Sentiment-Analysis
   

2. Set Up Python Environment

    python3 -m venv venv
    venv\Scripts\activate
   

3. Install Dependencies from requirements.txt:

   pip install -r requirements.txt

Running the project-

Ensure that you are in the project home directory. Create the machine learning model by running below command - python model.py This would create a serialized version of our model into a file model.pkl

Run app.py using below command to start Flask API python app.py By default, flask will run on port 5000. Navigate to url http://localhost:5000

Model Training

1. Run the script to train the model and display performance metrics: python sentiment_analysis.py

2. View the final_df DataFrame to see the original reviews with their classified sentiments.

3. Use the saved model.pkl and cv-model.pkl files for predictions on new data.

The script generates:

1. Accuracy score and confusion matrix for the trained model.
2. A DataFrame (final_df) mapping original reviews to their classified sentiments.

Example of final_df:

Review	Second Header
The food was amazing!	Positive
Terrible service and cold food	Negative