Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT

Mehtab Syed, Elena Arsevska, Mathieu Roche, Maguelonne Teisseire

2022

Abstract

In the first quarter of 2020, the World Health Organization (WHO) declared COVID-19 a public health emergency around the globe. Different users from all over the world shared their opinions about COVID-19 on social media platforms such as Twitter and Facebook. At the beginning of the pandemic, it became relevant to assess public opinions regarding COVID-19 using data available on social media. We used a recently proposed hierarchy-based measure for tweet analysis (H-TFIDF) for feature extraction over sentiment classification of tweets. We assessed how H-TFIDF and concatenation of H-TFIDF with bidirectional encoder representations from transformers (BH-TFIDF) perform over state-of-the-art bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF) features for sentiment classification of COVID-19 tweets. A uniform experimental setup of the training-test (90% and 10%) split scheme was used to train the classifier. Moreover, evaluation was performed with the gold standard expert labeled dataset to measure precision for each binary classified class.

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Paper Citation


in Harvard Style

Syed M., Arsevska E., Roche M. and Teisseire M. (2022). Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF, ISBN 978-989-758-552-4, pages 648-656. DOI: 10.5220/0010887800003123


in Bibtex Style

@conference{healthinf22,
author={Mehtab Syed and Elena Arsevska and Mathieu Roche and Maguelonne Teisseire},
title={Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF,},
year={2022},
pages={648-656},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010887800003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF,
TI - Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT
SN - 978-989-758-552-4
AU - Syed M.
AU - Arsevska E.
AU - Roche M.
AU - Teisseire M.
PY - 2022
SP - 648
EP - 656
DO - 10.5220/0010887800003123