Emotion Detection From Tweets Using a BERT and SVM Ensemble Model

Ionuţ-Alexandru Albu, Stelian Spînu
8/9/2022
cs.CL

No official code found. Best implementation: alexalbu98/Emotion-Detection-From-Tweets-Using-BERT-and-SVM-Ensemble-Model (3 stars, MIT, last commit 2022-07-10).

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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Code Implementations(1)

alexalbu98/Emotion-Detection-From-Tweets-Using-BERT-and-SVM-Ensemble-ModelCommunity100%
MIT

A BERT and SVM approach for the task of emotion detection from tweets

33Jupyter NotebookJun 19, 20214 years agoMIT

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Cite this paper

@article{albu2022emotion,
  title  = {Emotion Detection From Tweets Using a BERT and SVM Ensemble Model},
  author = {Ionuţ-Alexandru Albu and Stelian Spînu},
  year   = {2022},
  eprint = {2208.04547},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.04547v1}
}

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