Automated Analysis of Text Messages Related to the Coronavirus Pandemic on Twitter

Student: Pozniak Daniil

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2021

This paper focuses on the topic of automated text analysis using the example of sentiment analysis of messages related to the coronavirus pandemic on Twitter. Similar studies of worldwide events have already been carried out earlier. The uniqueness of this work is in the analysis of user opinions on a new phenomenon for the modern world - the COVID-19 pandemic. The paper describes the principles of automatic analysis of text information, algorithms that solve the problem of text classification, compares the methods of text preprocessing and classification algorithms. The result of the work is an efficient model of classifying Twitter messages based on their sentiment, built and selected as a result of comparison. Results also contain analysis and visualization of the results of work of the model and data collected with Twitter API and data from open sources.

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