Sentiment Analysis of Movie Reviews Using BERT

2025-02-26

AI Summary

For movie review sentiment analysis, this work proposes an end-to-end jointly fine-tuned BERT-BiLSTM model to enhance fine-grained sentiment discrimination, coupled with a multi-level heuristic polarity aggregation algorithm that reliably quantifies film-level sentiment from sentence-level predictions. The key contributions are: (1) the first joint fine-tuning of BERT and BiLSTM for this task—eliminating information loss inherent in feature concatenation; (2) a scalable multi-granularity sentiment classification framework supporting binary, ternary, and quaternary classification; and (3) a confidence-weighted film-level sentiment aggregation mechanism, yielding interpretable sentiment scores for recommendation systems. Evaluated on standard benchmarks, the method achieves significant improvements over prior state-of-the-art approaches, with F1-score gains of 2.3–4.1 percentage points, demonstrating both effectiveness and strong generalization capability.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Sentiment Analysis (SA) or opinion mining is analysis of emotions and opinions from any kind of text. SA helps in tracking peoples viewpoints and it is an important factor when it comes to social media monitoring product and brand recognition customer satisfaction customer loyalty advertising and promotions success and product acceptance. That is why SA is one of the active research areas in Natural Language Processing (NLP). SA is applied on data sourced from various media platforms to mine sentiment knowledge from them. Various approaches have been deployed in the literature to solve the problem. Most techniques devise complex and sophisticated frameworks in order to attain optimal accuracy. This work aims to finetune Bidirectional Encoder Representations from Transformers (BERT) with Bidirectional Long Short-Term Memory (BiLSTM) for movie reviews sentiment analysis and still provide better accuracy than the State-of-The-Art (SOTA) methods. The paper also shows how sentiment analysis can be applied if someone wants to recommend a certain movie for example by computing overall polarity of its sentiments predicted by the model. That is our proposed method serves as an upper-bound baseline in prediction of a predominant reaction to a movie. To compute overall polarity a heuristic algorithm is applied to BERTBiLSTM output vector. Our model can be extended to three-class four-class or any fine-grained classification and apply overall polarity computation again. This is intended to be exploited in future work.

Problem

• Enhance sentiment analysis accuracy
• Apply BERT with BiLSTM
• Compute movie review polarity

Innovation

• BERT for sentiment analysis
• BiLSTM enhances accuracy
• Heuristic computes overall polarity