ABSTRACT
The coronavirus pandemic is one of the leading communication topics for users on social networks. It causes different emotions in people: fear, sadness, anger, joy, and elation. Detecting sentiment about the pandemic is an acute challenge because it helps track people’s attitudes about the pandemic itself and the messages and decisions of local authorities aimed at combating the coronavirus. To address the issue, namely natural language processing, messages are processed using the TextRank vectorization method and the SVM-based two-level classification model. The first stage is the detection of tweets that are directly related to the coronavirus. The second stage means detecting the sentiment of the dataset obtained in the first stage. The classifier’s effectiveness was tested using the following metrics: precision, recall, F1-norm, and confusion matrix, and averaged about 90%. Thus, the automated detection of the sentiment of Twitter messages about the coronavirus pandemic was obtained. The approach described in the paper will allow assessing public opinion on pandemic control measures applied by the country’s governments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.