# BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews

arXiv: https://arxiv.org/abs/2305.06595 · HTML: https://mlsanity.com/paper/2305.06595/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Sentiment analysis in Bengali via transfer learning using multi-lingual BERT](https://mlsanity.com/paper/2012.07538/) (2020)
- [Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models](https://mlsanity.com/paper/1610.00369/) (2016)

## Built on by (most cited first, 14 of 14)

- [Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis](https://mlsanity.com/paper/2308.10783/) (2023)
- [BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis](https://mlsanity.com/paper/2408.08964/) (2024)
- [BLP-2023 Task 2: Sentiment Analysis](https://mlsanity.com/paper/2310.16183/) (2023)
- [LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Language](https://mlsanity.com/paper/2311.12735/) (2023)
- [Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation](https://mlsanity.com/paper/2504.02293/) (2025)
- [ANUBHUTI: A Comprehensive Corpus For Sentiment Analysis In Bangla Regional Languages](https://mlsanity.com/paper/2506.21686/) (2025)
- [Semantic Label Drift in Cross-Cultural Translation](https://mlsanity.com/paper/2510.25967/) (2025)
- [Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions](https://mlsanity.com/paper/2401.07847/) (2024)
- [BiST: A Gold Standard Bangla-English Bilingual Corpus for Sentence Structure and Tense Classification with Inter-Annotator Agreement](https://mlsanity.com/paper/2604.04708/) (2026)
- [MultiSoc-4D: A Benchmark for Diagnosing Instruction-Induced Label Collapse in Closed-Set LLM Annotation of Bengali Social Media](https://mlsanity.com/paper/2605.06940/) (2026)
- [BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media Posts](https://mlsanity.com/paper/2310.09238/) (2023)
- [BanglaSentNet: An Explainable Hybrid Deep Learning Framework for Multi-Aspect Sentiment Analysis with Cross-Domain Transfer Learning](https://mlsanity.com/paper/2511.23264/) (2025)
- [BanglaLorica: Design and Evaluation of a Robust Watermarking Algorithm for Large Language Models in Bangla Text Generation](https://mlsanity.com/paper/2601.04534/) (2026)
- [Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration](https://mlsanity.com/paper/2607.23538/) (2026)

## Abstract (arXiv)

The analysis of consumer sentiment, as expressed through reviews, can provide a wealth of insight regarding the quality of a product. While the study of sentiment analysis has been widely explored in many popular languages, relatively less attention has been given to the Bangla language, mostly due to a lack of relevant data and cross-domain adaptability. To address this limitation, we present BanglaBook, a large-scale dataset of Bangla book reviews consisting of 158,065 samples classified into three broad categories: positive, negative, and neutral. We provide a detailed statistical analysis of the dataset and employ a range of machine learning models to establish baselines including SVM, LSTM, and Bangla-BERT. Our findings demonstrate a substantial performance advantage of pre-trained models over models that rely on manually crafted features, emphasizing the necessity for additional training resources in this domain. Additionally, we conduct an in-depth error analysis by examining sentiment unigrams, which may provide insight into common classification errors in under-resourced languages like Bangla. Our codes and data are publicly available at https://github.com/mohsinulkabir14/BanglaBook.