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

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.
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.
BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews
http://arxiv.org/abs/2305.06595v3