Title: EmoRile: a personalised emoji prediction scheme based on user profiling

Authors: Vandita Grover; Hema Banati

Addresses: Department of Computer Science, New Arts Faculty Building, University of Delhi, New Delhi, India; Department of Computer Science, Acharya Narendra Dev College, University of Delhi, New Delhi, India ' Department of Computer Science, Dyal Singh College, University of Delhi, New Delhi, India

Abstract: Emojis are widely used to express emotions and complement text communication. Existing approaches for emoji prediction are generic and generally utilise text or time for emoji prediction. However, research reveals that emoji usage differs among users. So individual users' preferences for certain emojis need to be captured while predicting emojis for them. In this paper, a novel emoji-usage-based profiling: EmoRile is proposed. In EmoRile, emoji-usage-based user profiles were created which could be accomplished by compiling a new dataset included users' information. Distinct models with different combinations of text, text sentiment, and users' preferred emojis were created for emoji prediction. These models were tested on various architectures with a very large emoji label space. Rigorous experimentation showed that even with a large label space, EmoRile predicted emojis with similar accuracy as compared to existing emoji prediction approaches with a smaller label space; making it a competitive emoji prediction approach.

Keywords: emojis in sentiment analysis; emoji prediction; user profile-based emojis.

DOI: 10.1504/IJBIDM.2023.130594

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.4, pp.470 - 485

Received: 20 Sep 2021
Accepted: 06 Jan 2022
Published online: 01 May 2023 *

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