Sentiment Analysis of Movie Reviews Using Natural Language Processing

Introduction

Sentiment analysis of movie reviews is a significant application of natural language processing (NLP) that involves determining the sentiment expressed in textual data. This project proposal outlines a framework for developing a sentiment analysis system that classifies movie reviews as positive or negative, leveraging machine learning techniques.

Background

Sentiment analysis has gained traction due to its ability to provide insights into public opinion and consumer preferences. Recent research has shown that NLP techniques, combined with machine learning models, can effectively classify sentiments in text data. The use of models such as Naive Bayes, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks has proven effective in capturing the nuances of human language.

Project Objective

The primary objective of this project is to develop a robust sentiment analysis system capable of accurately classifying movie reviews. The system will utilize advanced NLP techniques and machine learning models to improve classification accuracy.

Methodology

1. Data Collection and Preprocessing

• Dataset: Utilize the IMDb dataset, which contains 50,000 movie reviews labeled as positive or negative. The dataset is available at Kaggle.
• Preprocessing: Perform data cleaning by removing HTML tags, special characters, and stopwords. Convert text to lowercase and apply stemming to standardize the data.

2. Model Development

• Naive Bayes Classifier: Implement a Naive Bayes model using scikit-learn for initial sentiment classification.
• Deep Learning Models: Develop CNN and LSTM models to capture complex patterns in the text data. Utilize GloVe embeddings to enhance word representation.

3. Training and Evaluation

• Training: Train each model on the preprocessed dataset using appropriate loss functions and optimization techniques.
• Evaluation Metrics: Evaluate model performance using accuracy, precision, recall, and F1-score metrics.

Expected Outcomes

The proposed system is expected to achieve high accuracy in classifying movie reviews as positive or negative. By leveraging deep learning techniques and word embeddings, the system should effectively handle variations in language use across different reviews.

Conclusion

This project aims to advance sentiment analysis techniques by developing a state-of-the-art system for classifying movie reviews. The integration of traditional machine learning models with deep learning approaches is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Sentiment Analysis of Movie Reviews Using Natural Language Processing," available at ScienceDirect.