Emoji Prediction in Tweets using BERT

TL;DR

This paper presents a transformer-based method using BERT to predict emojis in tweets, achieving over 75% accuracy and advancing understanding of emoji usage in social media communication.

Contribution

It introduces a fine-tuned BERT model specifically trained for emoji prediction in tweets, outperforming existing models in accuracy.

Findings

Achieved over 75% accuracy in emoji prediction

Outperformed several state-of-the-art models

Demonstrated effectiveness of BERT in social media NLP tasks

Abstract

In recent years, the use of emojis in social media has increased dramatically, making them an important element in understanding online communication. However, predicting the meaning of emojis in a given text is a challenging task due to their ambiguous nature. In this study, we propose a transformer-based approach for emoji prediction using BERT, a widely-used pre-trained language model. We fine-tuned BERT on a large corpus of text (tweets) containing both text and emojis to predict the most appropriate emoji for a given text. Our experimental results demonstrate that our approach outperforms several state-of-the-art models in predicting emojis with an accuracy of over 75 percent. This work has potential applications in natural language processing, sentiment analysis, and social media marketing.