Data mining for public channels and groups in telegram messenger

O. K. Golovnin; D. E. Pleshanov; A. A. Stolbova

doi:10.1117/12.2669231

5 January 2023 Data mining for public channels and groups in telegram messenger

O. K. Golovnin, D. E. Pleshanov, A. A. Stolbova

Proceedings Volume 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022); 1256405 (2023) https://doi.org/10.1117/12.2669231
Event: Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2022), 2022, Dushanbe, Tajikistan

Abstract

The paper presents the developed approach and software system for the Data Mining of public channels and groups in the Telegram messenger, for which emotionally colored vocabulary is identified in the texts and the emotional assessment is calculated in relation to objects, processes, events, phenomena, i.e., the opinion of the author is formed. The opinions of the authors are divided into two types: direct opinion and comparison, and the tonality receives three main assessments: positive, negative, neutral. The software system is implemented in the Python using the Dostoevsky library. RuSentiment dataset was used for training. The software system evaluates public opinion based on the analysis of comments in public channels and groups, for which the software system searches for comments and analyzes them, generates results as graphs and statistical values. The developed software system was tested for certain search parameters, which showed the practical applicability of the solution. The software system evaluates the crowd reaction to a post or evaluate how they relate to any fact based on the comments under the post in Telegram.

Citation Download Citation

O. K. Golovnin, D. E. Pleshanov, and A. A. Stolbova "Data mining for public channels and groups in telegram messenger", Proc. SPIE 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022), 1256405 (5 January 2023); https://doi.org/10.1117/12.2669231

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