metadata

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.

License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

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 $1\leq r \leq 5$ 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 For a review sample $i$ with rating $r_{i}$ , the sentiment label $S_{i}$ is,
$S_i =\begin{cases} \text{Negative}, & \text{if } r_i \leq 2\\ \text{Neutral}, & \text{if } r_i = 3\\ \text{Positive}, & \text{if }r_i \geq 4 \end{cases}$
`label`	The numerical representation of the sentiment label For a review sample $i$ with sentiment label $S_{i}$ , the numerical label is,
$label_i = \begin{cases} 0, &\text{if } S_i = \text{Negative} \\ 1, &\text{if } S_i = \text{Neutral} \\ 2, &\text{if } S_i = \text{Positive} \\ \end{cases}$

Citation

If you find this work useful, please cite our paper:

@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.",
}