Emotion Detection From Tweets Using a BERT and SVM Ensemble Model
This addresses emotion recognition for social media analysis, but it is incremental as it builds on existing methods like BERT and SVM.
The paper tackled emotion detection in tweets by creating a balanced dataset with a neutral class and proposing an ensemble model combining BERT and SVM, achieving a state-of-the-art accuracy of 0.91.
Automatic identification of emotions expressed in Twitter data has a wide range of applications. We create a well-balanced dataset by adding a neutral class to a benchmark dataset consisting of four emotions: fear, sadness, joy, and anger. On this extended dataset, we investigate the use of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for emotion recognition. We propose a novel ensemble model by combining the two BERT and SVM models. Experiments show that the proposed model achieves a state-of-the-art accuracy of 0.91 on emotion recognition in tweets.