# Sentiment Classification in Bangla Textual Content: A Comparative Study

arXiv: https://arxiv.org/abs/2011.10106 · HTML: https://mlsanity.com/paper/2011.10106/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis](https://mlsanity.com/paper/2308.10783/) (2023)
- [Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings](https://mlsanity.com/paper/2410.13153/) (2024)
- [A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models](https://mlsanity.com/paper/2107.03844/) (2021)
- [Vacaspati: A Diverse Corpus of Bangla Literature](https://mlsanity.com/paper/2307.05083/) (2023)
- [BLP-2023 Task 2: Sentiment Analysis](https://mlsanity.com/paper/2310.16183/) (2023)
- [Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings](https://mlsanity.com/paper/2408.02237/) (2024)
- [RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers](https://mlsanity.com/paper/2310.14261/) (2023)
- [BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning](https://mlsanity.com/paper/2608.05104/) (2026)

## Abstract (arXiv)

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.