# Interpretable Bangla Sarcasm Detection using BERT and Explainable AI

arXiv: https://arxiv.org/abs/2303.12772 · HTML: https://mlsanity.com/paper/2303.12772/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 7 of 7)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Automatic Sarcasm Detection: A Survey](https://mlsanity.com/paper/1602.03426/) (2016)
- [Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study](https://mlsanity.com/paper/2104.14506/) (2021)
- [Indonesian Social Media Sentiment Analysis With Sarcasm Detection](https://mlsanity.com/paper/1505.03085/) (2015)
- [Demystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI](https://mlsanity.com/paper/2111.03890/) (2021)
- [BanglaSarc: A Dataset for Sarcasm Detection](https://mlsanity.com/paper/2209.13461/) (2022)
- [Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text and English Humor Literature](https://mlsanity.com/paper/2106.05752/) (2021)

## Built on by (most cited first, 2 of 2)

- [An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach](https://mlsanity.com/paper/2402.13871/) (2024)
- [Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection](https://mlsanity.com/paper/2311.18778/) (2023)

## Abstract (arXiv)

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.