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 this https URL

本研究旨在解决对于孟加拉语资源与跨领域适应性缺乏研究的限制，因此提出一个大规模的孟加拉语电子书评论数据集， 使用各种机器学习模型分析数据，发现预训练模型比手工特征模型具有更高的性能，同时进行错误分析以提供关于在孟加拉语等欠资源语言中常见的分类错误的见解。