Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple...

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Bibliographic Details
Published in:Scientific reports 2024-05, Vol.14 (1), p.12204-24, Article 12204
Main Authors: Singh, Gopendra Vikram, Ghosh, Soumitra, Firdaus, Mauajama, Ekbal, Asif, Bhattacharyya, Pushpak
Format: Article
Language:English
Subjects:
639/705/1042
639/705/117
Code switching
Datasets
Emojis
Emotions
English language
Hindi language
Humanities and Social Sciences
Mixed languages
multidisciplinary
Natural language processing
Science
Science (multidisciplinary)
Sentiment analysis
Social media
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Summary:In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI , which extends the SemEval 2020 Task 9 SentiMix dataset. Our approach is based on exploiting the relationship between emotion, sentiment, and emojis to build an end-to-end framework. We replace the self-attention sublayers in the transformer encoder with simple linear transformations and use the RMS-layer norm instead of the normal layer norm. Moreover, we employ Gated Linear Unit and Fully Connected layers to predict emojis and identify the emotion and sentiment of a tweet. Our experimental results on the SENTIMOJI dataset demonstrate that the proposed multi-task framework outperforms the single-task framework. We also show that emojis are strongly linked to sentiment and emotion and that identifying sentiment and emotion can aid in accurately predicting the most suitable emoji. Our work contributes to the field of natural language processing and can help in the development of more effective tools for sentiment analysis and emotion recognition in code-mixed languages. The codes and data will be available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#SENTIMOJI to facilitate research.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-58944-5