RNN-based Movie Review Sentiment Analysis

Project overview

A movie-review study comparing recurrent models for sentiment classification and rating prediction with separated evaluation data.

A bilingual movie-review study comparing recurrent networks for sentiment classification and rating regression. The workflow emphasises consistent English and Malay preprocessing, leakage-aware splits and task-appropriate evaluation.

Inside this build

• Created 80/10/10 training, validation and test splits before fitting the modelling workflow.
• Cleaned English and Malay reviews through lowercasing, punctuation handling, stop-word removal, lemmatisation, tokenisation and padding.
• Applied seeded augmentation only to training data, expanding classification examples from 222 to 888 and regression examples from 501 to 2,004.
• Compared SimpleRNN, LSTM and GRU models for binary sentiment and numerical rating prediction.
• Evaluated classification with accuracy, precision, recall and F1, and regression with error and fit metrics across rating bands without claiming an architecture winner.
• Added 22 tests with 55% coverage and documented missing-score, duplicate, short-review and repeated-text cleanup.

Source code & links