Considerations on sentiment of social network posts as a feature of destructive impacts

Abstract

Nowadays, people spend a lot of time in the information space, communicating within various social platforms. Content of those platforms can influence people’s feelings and personalities, which is especially relevant for young people. In this research, we made an attempt to prove this hypothesis. For the experiment, we selected the VKontakte social network and analysed users profiles together with the results of the psychological tests passed by them. The goal of the experiment was to find correlations between the information provided within the social network communities and the users’ personalities. Moreover, in this paper, we made an attempt to enhance the results of the classifier accuracy using the sentiment analysis. The experiments were conducted to test the sentiment analysis models, to analyse the proposed feature based on posts’ sentiment, and test the classifier for the detection of the potentially destructive impacts. The analysis of the correlation of the proposed feature with the communities that have potentially destructive impacts on anxiety is conducted. The analysis of the obtained results is provided. During the experiments, the authors found out that consideration of the posts’ sentiment allows increasing accuracy of the classifier for anxiety destructive impacts on 12.24 %. Additionally, we analysed the relationship between the user sentiments metric and destructiveness. We confirmed that the assessment of the user’s posts’ sentiment can be used to compile his psychological characteristics and determine possibility of destructiveness.

Keywords

Social network destructive impact machine learning protection from information Ammon’s test

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