mlsanityData to 7 Oct 2026

Fine-grained Sentiment Classification using BERT

arXiv 1910.03474 ↗First posted 4 Oct 2019cs.CL cs.LG stat.MLcited by 38

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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.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Oct 2019.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 5; 2024: 3; 2025: 5; 2026: 5.

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  1. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 410
  2. 2021Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classificationcited by 43
  3. 2021Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detectioncited by 38
  4. 2025GRAM: A Generative Foundation Reward Model for Reward Generalizationcited by 25
  5. 2021Intersectional Bias in Causal Language Modelscited by 24
  6. 2022Kallima: A Clean-label Framework for Textual Backdoor Attackscited by 22
  7. 2022Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learningcited by 13
  8. 2024Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classificationcited by 7
  9. 2021Active learning for reducing labeling effort in text classification taskscited by 4
  10. 2023Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Datacited by 4
  11. 2023DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discoverycited by 4
  12. 2021SMILE: Self-Distilled MIxup for Efficient Transfer LEarningcited by 3
26 more
  1. 2021Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixupcited by 3
  2. 2022YATO: Yet Another deep learning based Text analysis Open toolkitcited by 2
  3. 2022Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probescited by 2
  4. 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweetscited by 2
  5. 2022Understanding BLOOM: An empirical study on diverse NLP taskscited by 2
  6. 2025Sentiment Analysis of Movie Reviews Using BERTcited by 2
  7. 2022CNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classificationcited by 1
  8. 2023How to choose "Good" Samples for Text Data Augmentationcited by 1
  9. 2023Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglementcited by 1
  10. 2025Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysiscited by 1
  11. 2026Multilingual Reference Need Assessment System for Wikipediacited by 1
  12. 2021Distilling BERT for low complexity network trainingcited by 0
  13. 2021T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERTcited by 0
  14. 2021A Comprehensive Review on Summarizing Financial News Using Deep Learningcited by 0
  15. 2022Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysiscited by 0
  16. 2022Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Commentscited by 0
  17. 2022Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactionscited by 0
  18. 2023Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysiscited by 0
  19. 2024Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoderscited by 0
  20. 2024Invisible Textual Backdoor Attacks based on Dual-Triggercited by 0
  21. 2025Tricks and Plug-ins for Gradient Boosting with Transformerscited by 0
  22. 2025Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learningcited by 0
  23. 2026LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machinescited by 0
  24. 2026The Association of Transformer-based Sentiment Analysis with Symptom Distress and Deterioration in Routine Psychotherapy Carecited by 0
  25. 2026ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Taskscited by 0
  26. 2026Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimescited by 0