Summary of Enhancing Sentiment Analysis in Bengali Texts: a Hybrid Approach Using Lexicon-based Algorithm and Pretrained Language Model Bangla-bert, by Hemal Mahmud and Hasan Mahmud
Enhancing Sentiment Analysis in Bengali Texts: A Hybrid Approach Using Lexicon-Based Algorithm and Pretrained Language Model Bangla-BERT
by Hemal Mahmud, Hasan Mahmud
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses a gap in sentiment analysis (SA) research for the Bengali language, focusing on fine-grained sentiment categorization. A novel approach integrates rule-based algorithms with pre-trained language models to develop a system capable of generating sentiment scores and classifying reviews into nine distinct categories. The system is evaluated using BanglaBERT, a pre-trained transformer-based language model, and outperforms the standalone model in terms of accuracy, precision, and nuanced classification. This research demonstrates the value of combining rule-based and pre-trained language model approaches for enhanced SA in Bengali and suggests pathways for future application in languages with similar linguistic complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to understand how people feel when they write something. It’s like trying to figure out what someone means by their words. Right now, we don’t have many tools that can do this well in the Bengali language. The researchers made a new system that combines two different ways of understanding text: one based on rules and another based on learning from lots of examples. They tested it with a big collection of reviews and found that it worked better than just using one method alone. This is important because it could help us understand people’s feelings in other languages too. |
Keywords
» Artificial intelligence » Classification » Language model » Precision » Transformer
