Understanding Textual Emotion Through Emoji Prediction
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916898244919296 |
|---|---|
| author | Gordon, Ethan Kuppa, Nishank Tummala, Rigved Anasuri, Sriram |
| author_facet | Gordon, Ethan Kuppa, Nishank Tummala, Rigved Anasuri, Sriram |
| contents | This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and regularization techniques. Results show BERT achieves the highest overall performance due to its pre-training advantage, while CNN demonstrates superior efficacy on rare emoji classes. This research shows the importance of architecture selection and hyperparameter tuning for sentiment-aware emoji prediction, contributing to improved human-computer interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Textual Emotion Through Emoji Prediction Gordon, Ethan Kuppa, Nishank Tummala, Rigved Anasuri, Sriram Computation and Language Artificial Intelligence Machine Learning Neural and Evolutionary Computing This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and regularization techniques. Results show BERT achieves the highest overall performance due to its pre-training advantage, while CNN demonstrates superior efficacy on rare emoji classes. This research shows the importance of architecture selection and hyperparameter tuning for sentiment-aware emoji prediction, contributing to improved human-computer interaction. |
| title | Understanding Textual Emotion Through Emoji Prediction |
| topic | Computation and Language Artificial Intelligence Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2508.10222 |