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Methods for Automatic Analysis of COVID-19 Discussions on Internet Resources

Student: Chaikina Kristina

Supervisor: Dmitry Ilvovsky

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

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2021

In this work, the issue of analyzing the discussions rate of COVID-19 on Internet resources is studied. The aim of the work is to write a program that analyzes the occurrence frequency of articles containing information about the COVID-19 infection , compared to the total number of published articles for a selected period of time, based on an algorithmic approach to document processing that combines approaches to text data processing. The analysis was carried out on data extracted from three popular news resources, as well as messages from the social network Twitter were checked. In the course of the work, the features of preprocessing text data were described, as well as algorithms for processing them to obtain the desired result. The development was carried out in the Python programming language in the Jupiter Notebook environment. The result of the work is an application that implements models for processing text information with subsequent analysis of the results obtained and justification of the effectiveness of the approach.

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