Abstract
AbstractEmojis are a succinct and visual way to express feelings, emotions, and thoughts during text conversations. Owing to the increase in the use of social media, the usage of emojis has increased drastically. There are various techniques for automating emoji prediction, which use contextual information, temporal information, and user-based features. However, the problem of personalised and dynamic recommendations of emojis persists. This paper proposes personalised emoji recommendations using the time and location parameters. It presents a new annotated conversational dataset and investigates the impact of time and location for emoji prediction. The methodology comprises a hybrid model that uses neural networks and score-based metrics: semantic and cosine similarity. Our approach differs from existing studies and improves the accuracy of emoji prediction up to 73.32% using BERT. KeywordsNLPEmoji predictionBERT
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