IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation

Kurniawan, Sandy; Budi, Indra; Ibrohim, Muhammad Okky

doi:10.18653/v1/2020.semeval-1.263

Cited by 3 publications

References 15 publications

(17 reference statements)

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

Singh,

Ghosh,

Firdaus

et al. 2024

Sci Rep

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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.

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

Singh,

Ghosh,

Firdaus

et al. 2024

Sci Rep

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Unity in Diversity: Multilabel Emoji Identification in Tweets

Singh

Firdaus

Ekbal

et al. 2023

IEEE Trans. Comput. Soc. Syst.

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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

Zampieri

Nakov²,

Rosenthal

et al. 2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

234

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We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.

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IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation

Cited by 3 publications

References 15 publications

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

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

Unity in Diversity: Multilabel Emoji Identification in Tweets

SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

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