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Emotion Detection From Tweets Using a BERT and SVM Ensemble Model

2022-08-09 05:32:29
Ionuţ-Alexandru Albu, Stelian Spînu
arXiv_CL
             
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2208.04547

PDF

https://arxiv.org/pdf/2208.04547.pdf

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