Emoji Prediction in Tweets using BERT
| Autor: | Nusrat, Muhammad Osama, Habib, Zeeshan, Alam, Mehreen, Jamal, Saad Ahmed |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: This paper is focused on predicting emojis corresponding to tweets using BERT |
| Databáze: | arXiv |
| Externí odkaz: |
|