Interpretable Bangla Sarcasm Detection using BERT and Explainable AI
Ramisa Anan, Tasnim Sakib Apon, Zeba Tahsin Hossain, Elizabeth Antora, Modhu, Sudipta Mondal, MD. Golam Rabiul Alam

TL;DR
This paper introduces a BERT-based model for Bangla sarcasm detection achieving high accuracy, enhanced with explainability via LIME, and utilizes a newly collected dataset from social media comments.
Contribution
It presents the first BERT-based sarcasm detection system for Bangla, combined with explainability techniques and a new dataset for evaluation.
Findings
BERT-based model achieves 99.60% accuracy.
Traditional machine learning algorithms reach 89.93% accuracy.
Explainability is added using LIME to interpret model decisions.
Abstract
A positive phrase or a sentence with an underlying negative motive is usually defined as sarcasm that is widely used in today's social media platforms such as Facebook, Twitter, Reddit, etc. In recent times active users in social media platforms are increasing dramatically which raises the need for an automated NLP-based system that can be utilized in various tasks such as determining market demand, sentiment analysis, threat detection, etc. However, since sarcasm usually implies the opposite meaning and its detection is frequently a challenging issue, data meaning extraction through an NLP-based model becomes more complicated. As a result, there has been a lot of study on sarcasm detection in English over the past several years, and there's been a noticeable improvement and yet sarcasm detection in the Bangla language's state remains the same. In this article, we present a BERT-based…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Natural Language Processing Techniques · Topic Modeling
