Abstract
The paper considers the posts’ sentiment in the social network communities in the context of correlation with possible destructive impacts. Under the destructive impacts the authors understand the impacts that can “provoke aggressive actions and aggressive behavior in relation to others or yourself”. The paper describes an approach to the determination of posts’ sentiment. The authors propose an additional feature for the detection of destructive impacts of social network communities based on posts’ sentiment. 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%.
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This research is being supported by the grant of RSF #21-71-20078 in SPC RAS.
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Gaifulina, D., Branitskiy, A., Levshun, D., Doynikova, E., Kotenko, I. (2023). Sentiment Analysis of Social Network Posts for Detecting Potentially Destructive Impacts. In: Braubach, L., Jander, K., Bădică, C. (eds) Intelligent Distributed Computing XV. IDC 2022. Studies in Computational Intelligence, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-031-29104-3_23
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