Abstract
COVID-19 has induced anxiety, depression, and fear among people around the world with its cases. During this period, people undergo mixed emotion. Social media is a tool that affected human life during this time in a dominant manner. Twitter is a trending social media platform. Analyzing sentiment of tweets related to COVID-19 can help to analyze the sentiments around the world. In this system, we have taken the dataset which contains tweets related to COVID-19 from IEEE dataport. SVM and LSTM models are built which classifies the tweets as positive, negative, and neutral accordingly. The performance of LSTM model is further analyzed by using hyperparameter tuning method. LSTM gave better results than SVM. It gave an accuracy of 94.58%.
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Nirmala Devi, K., Shanthi, S., Hemanandhini, K., Haritha, S., Aarthy, S. (2022). Analysis of COVID-19 Epidemic Disease Dynamics Using Deep Learning. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_31
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DOI: https://doi.org/10.1007/978-981-19-2948-9_31
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