Movies Sentiment App
Movies Sentiment App
Movies Sentiment App is a machine-learning powered application built to classify movie reviews into positive or negative sentiments. The system uses TF-IDF vectorization, Chi-square feature selection, and a Support Vector Machine (SVM) classifier to generate accurate sentiment predictions. The machine learning model and its full training pipeline are released as an open-source repository for public use and learning.
Category
Machine Learning
Client
My Client
Start Date
June 2025
End Date
June 2025
Description
This application transforms raw movie review text into structured sentiment insights through a classical NLP pipeline. TF-IDF is used to represent word importance, while Chi-square filtering selects the most significant features before the data is passed to an SVM classifier. The result is a compact yet highly reliable model that performs well on text classification tasks. The project aims to demonstrate clean ML implementation while providing an open-source repository that others can review, reuse, or improve.
THE STORY
The idea started from exploring how traditional machine learning methods can still outperform or match modern deep learning techniques when applied correctly in sentiment classification. Movie reviews provide a diverse and expressive text dataset, making them ideal for experimentation. After testing multiple preprocessing methods and algorithms, TF-IDF + Chi-square + SVM consistently produced the best results with strong generalization. To contribute back to the community, I open-sourced the entire ML repository—dataset preparation, preprocessing scripts, model training, and evaluation—so others can learn from or build upon the work.
OUR APPROACH
My approach was to build a streamlined sentiment analysis pipeline that is easy to understand yet technically robust. The workflow includes text cleaning, TF-IDF transformation, Chi-square feature selection, and SVM model training with fine-tuned hyperparameters. The backend ML logic is documented and made publicly available through the open-source repository, allowing transparency and future extension. This combination of clarity, reproducibility, and practical performance makes the Movies Sentiment App a solid example of classical ML applied to real-world text analysis.