Abstract
The more societies develop, people have less time for interacting face-to-face with each other. Therefore, more and more users express their opinions on many topics on Twitter. The sentiments contained in these opinions are becoming a valuable source of data for politicians, researchers, producers, and celebrities. Many studies have used this data source to solve a variety of practical problems. However, most of the previous studies only focused on using the sentiment in tweets to address the issues regarding commercial without considering the negative aspects related to user psychology, such as psychological disorders, cyberbullying, antisocial behaviors, depression, and negative thoughts. These problems have a significant effect on users and societies. This paper proposes a method to detect the psychological tendency that hides insides one person and to give the causations that lead to this psychological tendency based on analyzing sentiment of tweets by combining the feature ensemble model and the convolutional neural network model. The results prove the efficacy of the proposed approach in terms of the \(F_1\) score and received information.
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This research was supported by the National Research Foundation of Korea (NRF) grant funded by the BK21PLUS Program (22A20130012009).
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Phan, H.T., Tran, V.C., Nguyen, N.T., Hwang, D. (2020). A Framework for Detecting User’s Psychological Tendencies on Twitter Based on Tweets Sentiment Analysis. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_32
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