A Multi-Agent System for guiding users in on-line social environments. (September 2020)
- Record Type:
- Journal Article
- Title:
- A Multi-Agent System for guiding users in on-line social environments. (September 2020)
- Main Title:
- A Multi-Agent System for guiding users in on-line social environments
- Authors:
- Aguado, G.
Julian, V.
Garcia-Fornes, A.
Espinosa, A. - 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 socialAbstract: 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. … (more)
- Is Part Of:
- Engineering applications of artificial intelligence. Volume 94(2020)
- Journal:
- Engineering applications of artificial intelligence
- Issue:
- Volume 94(2020)
- Issue Display:
- Volume 94, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 94
- Issue:
- 2020
- Issue Sort Value:
- 2020-0094-2020-0000
- Page Start:
- Page End:
- 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 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09521976 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engappai.2020.103740 ↗
- Languages:
- English
- ISSNs:
- 0952-1976
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3755.704500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13733.xml