Machine Learning-Based Sentiment Analysis System for IMDB Movie Reviews

This article introduces a complete NLP project that uses machine learning techniques to perform sentiment classification on IMDB movie reviews, covering the full workflow including text preprocessing, feature extraction, model training, and evaluation. The original author of the project is Shraddha Bankar, published on GitHub (Project title: IMDB_Movie_Reviews_Sentiment_Analysis, link: https://github.com/Shraddha-Bankar/IMDB_Movie_Reviews_Sentiment_Analysis) on June 9, 2026, under the MIT open-source license.

In the digital age, movie review websites accumulate massive amounts of user content, making manual analysis impractical. Sentiment analysis technology uses NLP and machine learning to automatically identify emotional tendencies and classify reviews as positive or negative. As one of the world's largest movie databases, IMDB's review data has extremely high research value. This project builds a complete sentiment analysis system, demonstrating the full machine learning workflow from raw text to sentiment prediction.

Text Preprocessing

Remove noise such as HTML tags and special symbols, convert to lowercase, tokenize, filter stop words, and perform stemming.

Feature Extraction

Adopt bag-of-words model, TF-IDF (to highlight distinguishing keywords), and N-gram (to capture the semantics of word combinations).

Model Selection and Training

Supports Naive Bayes (probabilistic classifier, suitable for large-scale data), Logistic Regression (linear classifier), SVM (strong generalization ability in high-dimensional space), and Random Forest (ensemble learning to improve accuracy).

Model Evaluation

Use accuracy, precision, recall, and F1 score to measure performance; avoid overfitting through cross-validation and hyperparameter tuning.

Sentiment Prediction

New reviews are preprocessed and feature-extracted before being input into the model, which outputs positive/negative classification and confidence level.

• Movie Industry Insights: Production companies analyze audience feedback in bulk to adjust marketing strategies.
• Intelligent Recommendation: Combine user ratings and review sentiment to build a precise recommendation engine.
• Public Opinion Monitoring: Track public reactions to new films in real time and identify reputation crises.
• Academic Research: Provide standardized benchmark datasets and experimental frameworks.

• End-to-End Workflow: A complete pipeline from raw data to prediction results, easy to understand and reproduce.
• Modular Design: Each stage is independently encapsulated, facilitating replacement of algorithms or preprocessing methods.
• Scalable Architecture: Supports integration of deep learning models such as BERT and RoBERTa.
• Open-Source Friendly: The MIT license allows free use and secondary development.

This project demonstrates the strong capabilities of traditional machine learning in the NLP field. Through systematic preprocessing and feature engineering, even simple models can achieve satisfactory results. Future exploration can focus on aspect-level sentiment analysis (identifying attitudes towards specific aspects such as plot and acting). For developers who are new to NLP and machine learning, this project provides a complete practical chain and is an ideal starting point for learning.