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Analysis of Social Media Community Followers ' Reactions to News Related to Covind-19 Using Attention Mechanisms

Student: Ekaterina Brylyova

Supervisor: Alexander Omelchenko

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Big Data Analysis for Business, Economy, and Society (Master)

Year of Graduation: 2021

The paper aims at analyzing the reactions of members of Internet communities to news about coronavirus using attention mechanisms. To perform the analysis, the literature, considering various approaches and methods for determining the sentiment of textual information, was studied and a technique for using attention mechanisms to improve the predictive values of the model was described. During practical research, 47501 comments to the news of the RIA Novosti VKontakte community were collected and processed. On the basis of a part of the annotated data (15,000 comments) from the collected sample, a bidirectional neural network LSTM with an attention layer was trained, the interpretation of which was shown using the special coloring of words with the highest weight or importance to determine the message sentiment. Using the resulting model, the comments remaining in the database (32,501 comments) written to posts from 5 news blocks were classified and the sentiment of subscribers' reactions was analyzed according to 4 characteristics: negative / positive, sarcasm, toxicity and panic within the framework of temporal and thematic dynamics.

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