Categorization of COVID-19 Twitter Data Based on an Aspect-Oriented Sentiment Analysis and Fuzzy Logic

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During times of disaster or epidemic, social media has emerged as a vital means of communication. It is difficult to examine the complete situational awareness via many elements and emotions to aid authorities due to the unpredictability of these calamities. Currently, systems for aspect recognition and sentiment analysis rely heavily on labelled data and require human curation of aspect categories. To analyze public opinion from a variety of angles, this study suggested a hybrid text analytical approach. Using the popular Latent Dirichlet Allocation (LDA) topic modeling, we first extracted and clustered the elements from the data. We then used the linguistic inquiry and word count (LIWC) lexicon to extract the sentiments and label the dataset. Finally, in the third layer of our structure, we mapped the elements into emotions, and the sentiments were classified using well-known machine learning classifiers. The comparison of our technique with other aspect-oriented sentiment analysis approaches shows encouraging results in experiments with actual datasets, and our method with several variants of classifiers surpasses current methods with top F1 scores of 91%.