The Review Sentiment Analysis system is a Natural Language Processing (NLP) project designed to automatically analyze and interpret customer reviews by determining the sentiment behind them—whether positive, negative, or neutral. This system helps businesses gain valuable insights from user feedback at scale, enabling data-driven decisions to improve products, services, and customer satisfaction.

The model processes customer reviews collected from various platforms (e.g., e-commerce sites, social media, or feedback forms), cleans the text data, and uses machine learning or deep learning algorithms to classify sentiment. The output can be visualized through dashboards, highlighting overall customer perception and identifying areas of concern or satisfaction.

Key Features:

• Preprocessing of text data (tokenization, stop word removal, etc.)

• Sentiment classification (positive, negative, neutral)

• Support for multiple ML/NLP models (Logistic Regression, SVM, Naive Bayes, LSTM)

• Integration with real-world datasets or APIs

• Dashboard for sentiment visualization and analytics

• Scalable for analyzing thousands of reviews

Technologies Used:

• Languages & Libraries: Python, NLTK, TextBlob, Scikit-learn, TensorFlow/Keras (for deep learning models)

• NLP Tools: SpaCy, Vader Sentiment, BERT (optional for advanced use cases)

• Backend: Flask / Django

• Data Visualization: Matplotlib, Seaborn, Plotly, Dash

• Deployment: AWS / Heroku / Streamlit