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      Emotion Detection From Tweets Using a BERT and SVM Ensemble Model

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          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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          Author and article information

          Journal
          09 August 2022
          Article
          2208.04547
          00fd4e16-26b4-45f6-87d7-fc250c236628

          http://creativecommons.org/licenses/by-nc-sa/4.0/

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          U.P.B. Sci. Bull., Series C, Vol. 84, Iss. 1, 2022 ISSN 2286-3540
          cs.CL

          Theoretical computer science
          Theoretical computer science

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