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