Emotion Detection From Tweets Using a BERT and SVM Ensemble Model

• URL: http://arxiv.org/abs/2208.04547v1
• Date: Tue, 9 Aug 2022 05:32:29 GMT
• Title: Emotion Detection From Tweets Using a BERT and SVM Ensemble Model
• Authors: Ionu\c{t}-Alexandru Albu, Stelian Sp\^inu
• Abstract summary: We investigate the use of Support Vector Machine and Bidirectional Representations from Transformers 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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