BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews
This addresses the problem of limited resources for sentiment analysis in under-resourced languages like Bangla, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of Bangla sentiment analysis data by creating BanglaBook, a dataset of 158,065 book reviews with positive, negative, and neutral labels, and found that pre-trained models like Bangla-BERT significantly outperformed traditional methods.
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.