Sentiment Analysis of Movie and News reviews on Unbalanced Data Using Deep Learning with Abstract Feature Extraction
Shivani Rana, Rakesh Kanji, Shruti Jain · Recent Patents on Engineering · 2025
This study assessed a sentiment analysis model using a BERT-based deep learning approach, combined with a feedforward neural network (FFNN), designed to handle unbalanced datasets. The model was evaluated on the Rotten Tomatoes movie review dataset and the 20 Newsgroups dataset. Our method achieved an outstanding performance, with perfect accuracy of 100% and minimal loss (0.001 training and 0.010 testing loss) on the Rotten Tomatoes dataset and similar results on the 20 Newsgroups dataset. Key metrics, such as the Jaccard index, Hamming loss, Cohen’s kappa, mean squared error, and Matthews correlation coefficient (MCC), demonstrated its effectiveness, particularly in managing class imbalance and capturing nuanced sentiment variations. Comparative analysis showed the proposed model to outperform existing methods, highlighting its robustness and potential application in sentiment classification tasks across various textual data domains.