Interpretable Bangla Sarcasm Detection using BERT and Explainable AI

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 system that can achieve 99.60\% while the utilized traditional machine learning algorithms are only capable of achieving 89.93\%. Additionally, we have employed Local Interpretable Model-Agnostic Explanations that introduce explainability to our system. Moreover, we have utilized a newly collected bangla sarcasm dataset, BanglaSarc that was constructed specifically for the evaluation of this study. This dataset consists of fresh records of sarcastic and non-sarcastic comments, the majority of which are acquired from Facebook and YouTube comment sections.