A Multi-Agent System for guiding users in on-line social environments. (September 2020)

• Record Type: Journal Article
• Authors: Aguado, G.; Julian, V.; Garcia-Fornes, A.; Espinosa, A.
• Is Part Of: Engineering applications of artificial intelligence. Volume 94(2020)
• Publication Date: 2020-09
• Subjects: Multi-Agent System -- Social networks -- Sentiment analysis -- Stress analysis; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103740
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The present work is a study of the detection of negative affective or emotional states, the high-stress levels that people have using social network sites (SNSs), and the effect that this negative state or stress level has on the repercussions of posted messages. We aim to discover to what extent a user that has a state detected as negative by an analyzer (Sentiment analyzer and Stress analyzer) can affect other users and generate negative repercussions, and also determine whether it is more suitable to predict a future negative situation using different analyzers. We propose two different methods for creating a combined model of sentiment and stress, and we use them in our experimentation to discern which one is more suitable for predicting future negative situations that could arise from the interaction between users, and in what context. Additionally, we designed a Multi-Agent System (MAS) that integrates the analyzers to protect or advise users on a SNS. We have conducted this study to help build future systems that prevent negative situations where a user that has a negative state creates a repercussion in the SNS. This can help users avoid getting into a bad mood or help avoid privacy issues (e.g. a user that has a negative state posting information that the user does not really want to post). Highlights: Design and implementation of sentiment, stress, and combined analyses on texts. Proposal of a Multi-Agent system (MAS) for user guiding in on-line social sites. Integration of the different analyses implemented in the MAS. Experiments to discover the most useful analyses to predict future bad outcomes. Experiments to test our MAS in a real-life environment.