# Fine-grained Sentiment Classification using BERT

arXiv: https://arxiv.org/abs/1910.03474 · HTML: https://mlsanity.com/paper/1910.03474/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 30 of 38)

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- [Intersectional Bias in Causal Language Models](https://mlsanity.com/paper/2107.07691/) (2021)
- [Kallima: A Clean-label Framework for Textual Backdoor Attacks](https://mlsanity.com/paper/2206.01832/) (2022)
- [Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning](https://mlsanity.com/paper/2210.07733/) (2022)
- [Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification](https://mlsanity.com/paper/2406.01283/) (2024)
- [Active learning for reducing labeling effort in text classification tasks](https://mlsanity.com/paper/2109.04847/) (2021)
- [Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data](https://mlsanity.com/paper/2303.07484/) (2023)
- [DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery](https://mlsanity.com/paper/2310.10151/) (2023)
- [SMILE: Self-Distilled MIxup for Efficient Transfer LEarning](https://mlsanity.com/paper/2103.13941/) (2021)
- [Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup](https://mlsanity.com/paper/2109.07177/) (2021)
- [YATO: Yet Another deep learning based Text analysis Open toolkit](https://mlsanity.com/paper/2209.13877/) (2022)
- [Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes](https://mlsanity.com/paper/2210.01258/) (2022)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)
- [Understanding BLOOM: An empirical study on diverse NLP tasks](https://mlsanity.com/paper/2211.14865/) (2022)
- [Sentiment Analysis of Movie Reviews Using BERT](https://mlsanity.com/paper/2502.18841/) (2025)
- [CNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classification](https://mlsanity.com/paper/2209.06344/) (2022)
- [How to choose "Good" Samples for Text Data Augmentation](https://mlsanity.com/paper/2302.00894/) (2023)
- [Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement](https://mlsanity.com/paper/2309.09799/) (2023)
- [Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis](https://mlsanity.com/paper/2502.20682/) (2025)
- [Multilingual Reference Need Assessment System for Wikipedia](https://mlsanity.com/paper/2603.17146/) (2026)
- [Distilling BERT for low complexity network training](https://mlsanity.com/paper/2105.06514/) (2021)
- [T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT](https://mlsanity.com/paper/2106.01097/) (2021)
- [A Comprehensive Review on Summarizing Financial News Using Deep Learning](https://mlsanity.com/paper/2109.10118/) (2021)
- [Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis](https://mlsanity.com/paper/2202.10884/) (2022)
- [Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments](https://mlsanity.com/paper/2206.12262/) (2022)
- [Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions](https://mlsanity.com/paper/2211.03923/) (2022)
- [Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis](https://mlsanity.com/paper/2311.16965/) (2023)

## Abstract (arXiv)

Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in natural language processing in the process.