Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-58944-5 |
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| author | Gopendra Vikram Singh Soumitra Ghosh Mauajama Firdaus Asif Ekbal Pushpak Bhattacharyya |
| author_facet | Gopendra Vikram Singh Soumitra Ghosh Mauajama Firdaus Asif Ekbal Pushpak Bhattacharyya |
| author_sort | Gopendra Vikram Singh |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-966657643b284222b6bbd10f9a6da9ae |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-966657643b284222b6bbd10f9a6da9ae2024-11-17T12:24:06ZengNature PortfolioScientific Reports2045-23222024-05-0114112410.1038/s41598-024-58944-5Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments frameworkGopendra Vikram Singh0Soumitra Ghosh1Mauajama Firdaus2Asif Ekbal3Pushpak Bhattacharyya4Department of Computer Science and Engineering, Indian Institute of Technology PatnaDepartment of Computer Science and Engineering, Indian Institute of Technology PatnaUniversity of AlbertaDepartment of Computer Science and Engineering, Indian Institute of Technology PatnaDepartment of Computer Science and Engineering, Indian Institute of Technology BombayAbstract 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.https://doi.org/10.1038/s41598-024-58944-5 |
| spellingShingle | Gopendra Vikram Singh Soumitra Ghosh Mauajama Firdaus Asif Ekbal Pushpak Bhattacharyya Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework Scientific Reports |
| title | Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework |
| title_full | Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework |
| title_fullStr | Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework |
| title_full_unstemmed | Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework |
| title_short | Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework |
| title_sort | predicting multi label emojis emotions and sentiments in code mixed texts using an emojifying sentiments framework |
| url | https://doi.org/10.1038/s41598-024-58944-5 |
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