Feature selection for sentiment classification of COVID-19 tweets: H-TFIDF featuring BERT

Syed Mehtab Alam, Arsevska Elena, Roche Mathieu, Teisseire Maguelonne. 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, BIOSTEC 2022, Volume 5: HEALTHINF. Bier Nathalie (ed.), Fred Ana L. N. (ed.), Gamboa Hugo (ed.). Setúbal : Scitepress, 648-656. ISBN 978-989-758-552-4 International Joint Conference on Biomedical Engineering Systems and Technologies. 15, s.l., 9 Février 2022/11 Février 2022.

Note générale : Le congrès s'est tenu en ligne

Résumé : 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 as- sess 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 classifi- cation 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 fre- quency (TF-IDF) features for sentiment classification of COVID-19 tweets. A uni- form 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 ex- pert labelled dataset to measure precision for each binary classified class.

Mots-clés libres : Text Mining, Sentiment analysis, Twitter, COVID-19

Agences de financement européennes : European Commission

Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context

Auteurs et affiliations

• Syed Mehtab Alam, CIRAD-ES-UMR TETIS (FRA)
• Arsevska Elena, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0002-6693-2316
• Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
• Teisseire Maguelonne, INRAE (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/600947/)

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