Sentiment Classification in Bangla Textual Content: A Comparative Study

TL;DR

This study compares classical and deep learning sentiment classifiers for Bangla, revealing transformer models outperform others and providing resources for future research in low-resource language sentiment analysis.

Contribution

It introduces the first comprehensive comparison of models for Bangla sentiment analysis and provides publicly available datasets, model benchmarks, and a lexicon list for reproducibility.

Findings

Transformer models outperform classical algorithms in accuracy.

Deep learning models require more computational resources.

Public datasets and lexicons are now available for Bangla sentiment analysis.

Abstract

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, one of the prominent challenge is the lack of resources. Another important limitation, in the 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 classifiers 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. The presented results can be used for future studies as a benchmark.