"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings

TL;DR

This paper introduces a deep learning approach using bilingual word embeddings to detect sarcasm in Hindi-English code-mixed tweets, achieving state-of-the-art accuracy of 78.49%.

Contribution

It presents a new dataset and bilingual embeddings specifically designed for sarcasm detection in code-mixed social media text, improving upon existing methods.

Findings

Attention-based Bi-LSTM achieved 78.49% accuracy.

Bilingual embeddings improved sarcasm detection performance.

Deep learning models outperformed previous state-of-the-art results.

Abstract

With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence. One such being the detection of sarcasm in the social media texts. We present a corpus of tweets for training custom word embeddings and a Hinglish dataset labelled for sarcasm detection. We propose a deep learning based approach to address the issue of sarcasm detection in Hindi-English code mixed tweets using bilingual word embeddings derived from FastText and Word2Vec approaches. We experimented with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention). We were able to outperform all state-of-the-art performances with our deep learning models, with attention based Bi-directional LSTMs giving the best performance exhibiting an accuracy of 78.49%.