Understanding Textual Emotion Through Emoji Prediction
This incremental work improves sentiment-aware emoji prediction for human-computer interaction applications.
The paper tackled emoji prediction from short text by comparing four deep learning architectures, finding that BERT performed best overall while CNN was more effective for rare emoji classes.
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.