--- license: cc-by-nc-sa-4.0 task_categories: - text-classification - zero-shot-classification language: - bn tags: - Sentiment Analysis - Book Reviews - Product Reviews - Bangla - Bengali - Dataset pretty_name: BanglaBook size_categories: - 100K<n<1M --- # BᴀɴɢʟᴀBᴏᴏᴋ: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews This repository contains the code, data, and models of the paper titled "BᴀɴɢʟᴀBᴏᴏᴋ: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews" published in the ***Findings of the Association for Computational Linguistics: ACL 2023***. [](https://arxiv.org/abs/2305.06595) [](https://aclanthology.org/2023.findings-acl.80/) [](https://tinyurl.com/gscholarbanglabook) [](https://www.researchgate.net/publication/370688086_BanglaBook_A_Large-scale_Bangla_Dataset_for_Sentiment_Analysis_from_Book_Reviews) [](https://aclanthology.org/2023.findings-acl.80.pdf) [](https://drive.google.com/file/d/1-UkYs_Rx11S7qKOfR-6rnO2VDp3W78vQ/view?usp=sharing) [](https://aclanthology.org/2023.findings-acl.80.mp4) **License:** Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International [](http://creativecommons.org/licenses/by-nc-sa/4.0/) ## Data Format Each row consists of a book review sample. The table below describes what each column signifies. Column Title | Description ------------ | ------------- `id` | The unique identification number of the sample `Book_Name` | The title of the book that has been evaluated by the review `Writer_Name` | The name of the book's author `Category` | The genre to which the book belongs `Rating` | A numerical value \\(r\\) such that \\(1\leq r \leq 5\\)<br>A score reflecting the reviewer's subjective assessment of the book's quality `Review` | The review text written by the reviewer `Site` | The name of the online bookshop `sentiment` | The conveyed sentiment and class label of the review<br>For a review sample \\(i\\) with rating \\(r_i\\), the sentiment label \\(S_i\\) is,<br> $$ S_i =\begin{cases} \text{Negative}, & \text{if } r_i \leq 2\\ \text{Neutral}, & \text{if } r_i = 3\\ \text{Positive}, & \text{if }r_i \geq 4 \end{cases} $$ `label` | The numerical representation of the sentiment label<br>For a review sample \\(i\\) with sentiment label \\(S_i\\), the numerical label is,<br> $$label_i = \begin{cases} 0, &\text{if } S_i = \text{Negative} \\ 1, &\text{if } S_i = \text{Neutral} \\ 2, &\text{if } S_i = \text{Positive} \\ \end{cases}$$ ## Citation If you find this work useful, please cite our paper: ```bib @inproceedings{kabir-etal-2023-banglabook, title = "{B}angla{B}ook: A Large-scale {B}angla Dataset for Sentiment Analysis from Book Reviews", author = "Kabir, Mohsinul and Bin Mahfuz, Obayed and Raiyan, Syed Rifat and Mahmud, Hasan and Hasan, Md Kamrul", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.80", pages = "1237--1247", abstract = "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.", } ```