@inproceedings{wadhawan-aggarwal-2021-towards,
title = "Towards Emotion Recognition in {H}indi-{E}nglish Code-Mixed Data: A Transformer Based Approach",
author = "Wadhawan, Anshul and
Aggarwal, Akshita",
editor = "De Clercq, Orphee and
Balahur, Alexandra and
Sedoc, Joao and
Barriere, Valentin and
Tafreshi, Shabnam and
Buechel, Sven and
Hoste, Veronique",
booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wassa-1.21",
pages = "195--202",
abstract = "In the last few years, emotion detection in social-media text has become a popular problem due to its wide ranging application in better understanding the consumers, in psychology, in aiding human interaction with computers, designing smart systems etc. Because of the availability of huge amounts of data from social-media, which is regularly used for expressing sentiments and opinions, this problem has garnered great attention. In this paper, we present a Hinglish dataset labelled for emotion detection. We highlight a deep learning based approach for detecting emotions using bilingual word embeddings derived from FastText and Word2Vec approaches in Hindi-English code mixed tweets. We experiment with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention), along with transformers like BERT, RoBERTa, and ALBERT. The transformer based BERT model outperforms all current state-of-the-art models giving the best performance with an accuracy of 71.43{\%}.",
}
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%0 Conference Proceedings
%T Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach
%A Wadhawan, Anshul
%A Aggarwal, Akshita
%Y De Clercq, Orphee
%Y Balahur, Alexandra
%Y Sedoc, Joao
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Buechel, Sven
%Y Hoste, Veronique
%S Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F wadhawan-aggarwal-2021-towards
%X In the last few years, emotion detection in social-media text has become a popular problem due to its wide ranging application in better understanding the consumers, in psychology, in aiding human interaction with computers, designing smart systems etc. Because of the availability of huge amounts of data from social-media, which is regularly used for expressing sentiments and opinions, this problem has garnered great attention. In this paper, we present a Hinglish dataset labelled for emotion detection. We highlight a deep learning based approach for detecting emotions using bilingual word embeddings derived from FastText and Word2Vec approaches in Hindi-English code mixed tweets. We experiment with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention), along with transformers like BERT, RoBERTa, and ALBERT. The transformer based BERT model outperforms all current state-of-the-art models giving the best performance with an accuracy of 71.43%.
%U https://aclanthology.org/2021.wassa-1.21
%P 195-202
Markdown (Informal)
[Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach](https://aclanthology.org/2021.wassa-1.21) (Wadhawan & Aggarwal, WASSA 2021)
ACL