Paper Accepted at ICCIT 2020 - Bangla Language Computing Research

Paper Accepted at ICCIT 2020

19th December 2020

Our paper “Sentiment Classification in Bangla Textual Content: A Comparative Study” accepted at ICCIT 2020. Congratulations authors: Md. Arid Hasan,  Jannatul Tajrin, Shammur Absar Chowdhury, and Firoj Alam. Another contribution that can enrich Bangla language computing research.
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Sentiment analysis has been widely used to understand our views on social and political agendas or user experiences over a product. It is one of the cores and well-researched areas in NLP. However, for low-resource languages, like Bangla, a prominent challenge is the lack of resources. Another important limitation in current literature for Bangla is the absence of comparable results due to the lack of a well-defined train/test split. In this study, we explore several publicly available sentiment labeled datasets and designed classifier using both classical and deep learning algorithms. In our study, the classical algorithms include SVM and Random Forest, and deep learning algorithms include CNN, FastText, and transformer-based models. We compare these models in terms of model performance and time-resource complexity. Our finding suggests transformer-based models, which have not been explored earlier for Bangla, outperform all other models. Furthermore, we created a weighted list of lexicon content based on the valence score per class. We then analyzed the content for high significance entries per class, in the datasets. For reproducibility, we make publicly available data splits and the ranked lexicon list and the presented results can be used in for future studies as a benchmark.


title={Sentiment Classification in Bangla Textual Content: A Comparative Study},
author={Md. Arid Hasan,  Jannatul Tajrin, Shammur Absar Chowdhury, and Firoj Alam},
booktitle={2020 23rd International Conference of Computer and Information Technology (ICCIT)},