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A self-attention hybrid emoji prediction model for code-mixed language: (Hinglish)

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Abstract

Emojis are an essential tool for communication, and various resource-rich languages such as English use emoji prediction systems. However, there is limited research on emoji prediction for resource-poor and code-mixed languages such as Hinglish (Hindi + English), the fourth most used code-mixed language globally. This paper proposes a novel Hinglish Emoji Prediction (HEP) dataset created using Twitter as a corpus and a hybrid emoji prediction model BiLSTM attention random forest (BARF) for code-mixed Hinglish language. The proposed BARF model combines deep learning features with machine learning classification. It begins with BiLSTM to capture the context and then proceeds to self-attention to extract significant texts. Finally, it uses random forest to categorize the features to predict an emoji. The self-attention mechanism aids learning since Hinglish, a code-mixed language, lacks proper grammatical rules. The combination of deep learning and machine learning algorithms and attention is novel to emoji prediction in the code-mixed language(Hinglish). Results on the HEP dataset indicate that the BARF model outperformed previous multilingual and baseline emoji prediction models. It achieved an accuracy of 61.14%, precision of 0.66, recall of 0.59, and F1 score of 0.59.

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Notes

  1. https://github.com/Himabindugssn/HEP.

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  1. Department of Computer Engineering, Delhi Technological University, Delhi, 110042, India

    Gadde Satya Sai Naga Himabindu, Rajat Rao & Divyashikha Sethia

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  1. Gadde Satya Sai Naga Himabindu
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  2. Rajat Rao
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  3. Divyashikha Sethia
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Correspondence to Gadde Satya Sai Naga Himabindu.

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Himabindu, G.S.S.N., Rao, R. & Sethia, D. A self-attention hybrid emoji prediction model for code-mixed language: (Hinglish). Soc. Netw. Anal. Min. 12, 137 (2022). https://doi.org/10.1007/s13278-022-00961-1

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