Abstract
COVID-19 has scattered around the world since end of year 2019. In the past two years, countless people who have recovered from COVID-19 will still have sequelae. The sequelae are called as long-COVID. Although many people recover without treatment, unfortunately they come across long-COVID effects for weeks or even months. This paper aims to find out the emotions of people towards long-COVID using a machine learning approach based on the data obtained from one of the most popular social media—Twitter. Furthermore, the study deployed the proposed model by developing a prototype named Long COVID Emotion Analyzer. Twitter data were collected using the hash tag Long COVID for a total of five months (i.e., May–September 2021), resulting in 97,098 clean tweets. IBM Watson Tone Analyzer was used to label the emotion of the tweets. (i.e., sadness, joy, fear, anger, analytical, tentative, confident, neutral). Several machine learning algorithms were used to train and test the dataset. Results indicate Logistic Regression with Unigram of Bag of Words to be the best predicting model, with accuracy = 88%, F1-score = 88%, Area Under Curve = 97%.
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Ramakrishnan, K., Balakrishnan, V., Han, G.J., Seong, N.K. (2023). Long COVID Emotion Analyzer: Using Machine Learning Approach. In: Kang, DK., Alfred, R., Ismail, Z.I.B.A., Baharum, A., Thiruchelvam, V. (eds) Proceedings of the 9th International Conference on Computational Science and Technology. ICCST 2022. Lecture Notes in Electrical Engineering, vol 983. Springer, Singapore. https://doi.org/10.1007/978-981-19-8406-8_42
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DOI: https://doi.org/10.1007/978-981-19-8406-8_42
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