BSc IT Project Guide: Movie Review Sentiment Analysis
1. Introduction
The Movie Review Sentiment Analysis project aims to classify movie reviews as positive, negative, or neutral based on their textual content. This project uses Natural Language Processing (NLP) techniques combined with machine learning models to analyze user-generated reviews and determine their sentiment.
2. Objectives
- Develop a web-based application for classifying movie
review sentiments.
- Utilize NLP techniques to preprocess and analyze textual data.
- Train a machine learning model on a labeled dataset of movie reviews.
- Provide visual insights such as sentiment distribution and most common words.
3. Tools and Technologies
- Programming Language: Python
- Framework: Flask/Django (for web interface)
- Libraries: Scikit-learn, NLTK, Pandas, Matplotlib, Seaborn
- Database: SQLite/MySQL
- Deployment: Heroku/Render
4. System Requirements
- Operating System: Windows/Linux/MacOS
- Python 3.x
- Web Browser
- 4GB RAM minimum
5. System Design
The system architecture consists of a frontend user interface where users can input movie reviews, a backend server that processes the input using an NLP pipeline, and a trained sentiment analysis model that returns the sentiment classification. The results are then displayed on the frontend.
6. Implementation
The application allows users to enter movie reviews, which are then tokenized, cleaned, and transformed into vector representations. A trained model such as Logistic Regression or Naive Bayes classifies the sentiment. The application displays the result along with sentiment confidence levels and a summary dashboard.
7. Conclusion
This project demonstrates how sentiment analysis can be effectively applied to movie reviews using NLP and machine learning. The final product is a practical application that provides insights into public opinion on films.