# Sentiment Analysis of Movie Reviews Using BERT

arXiv: https://arxiv.org/abs/2502.18841 · HTML: https://mlsanity.com/paper/2502.18841/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Entailment as Few-Shot Learner](https://mlsanity.com/paper/2104.14690/) (2021)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies](https://mlsanity.com/paper/2010.00951/) (2020)
- [Fine-grained Sentiment Classification using BERT](https://mlsanity.com/paper/1910.03474/) (2019)
- [Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition](https://mlsanity.com/paper/2010.03746/) (2020)
- [Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function](https://mlsanity.com/paper/2009.04007/) (2020)
- [Parallelizing Legendre Memory Unit Training](https://mlsanity.com/paper/2102.11417/) (2021)
- [Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation](https://mlsanity.com/paper/2201.08702/) (2022)
- [Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering](https://mlsanity.com/paper/1904.06652/) (2019)
- [DeText: A Deep Text Ranking Framework with BERT](https://mlsanity.com/paper/2008.02460/) (2020)
- [A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1801.06480/) (2018)
- [Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews](https://mlsanity.com/paper/1412.5335/) (2014)
- [How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies](https://mlsanity.com/paper/2104.10513/) (2021)

## Built on by (most cited first, 2 of 2)

- [Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis](https://mlsanity.com/paper/2502.20682/) (2025)
- [Domain-Generalization to Improve Learning in Meta-Learning Algorithms](https://mlsanity.com/paper/2508.09418/) (2025)

## Abstract (arXiv)

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.