A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

Abstract

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. Belgium: TIRO http://www.belspo.be/belspo/fedra/proj.asp?l=en&COD=TA/00/08Risks and Safety on the Internet: The Perspective of European Children: Full Findings and Policy Implications From the EU Kids Online Survey of 9–16 Year Olds and Their Parents in 25 Countries http://eprints.lse.ac.uk/33731/Vanderhoven, E., Schellens, T., & Valcke, M. (2014). Educating teens about the risks on social network sites. An intervention study in Secondary Education. Comunicar, 22(43), 123-132. doi:10.3916/c43-2014-12Christofides, E., Muise, A., & Desmarais, S. (2012). Risky Disclosures on Facebook. Journal of Adolescent Research, 27(6), 714-731. doi:10.1177/0743558411432635George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications, 149, 102447. doi:10.1016/j.jnca.2019.102447Zhang, C., Zeng, D., Li, J., Wang, F.-Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487. doi:10.1002/asi.21206Lu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011). Multi-aspect Sentiment Analysis with Topic Models. 2011 IEEE 11th International Conference on Data Mining Workshops. doi:10.1109/icdmw.2011.125Nasukawa, T., & Yi, J. (2003). Sentiment analysis. Proceedings of the international conference on Knowledge capture - K-CAP ’03. doi:10.1145/945645.945658Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proceedings of the 21st ACM international conference on Multimedia - MM ’13. doi:10.1145/2502081.2502282Deb, S., & Dandapat, S. (2019). Emotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions. IEEE Transactions on Affective Computing, 10(3), 360-373. doi:10.1109/taffc.2017.2730187Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013). Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. IEEE Intelligent Systems, 31(1), 48-55. doi:10.1109/mis.2015.98Rosaci, D., & Sarnè, G. M. L. (2014). Multi-agent technology and ontologies to support personalization in B2C E-Commerce. Electronic Commerce Research and Applications, 13(1), 13-23. doi:10.1016/j.elerap.2013.07.003Singh, A., & Sharma, A. (2017). MAICBR: A Multi-agent Intelligent Content-Based Recommendation System. Lecture Notes in Networks and Systems, 399-411. doi:10.1007/978-981-10-3920-1_41Villavicencio, C., Schiaffino, S., Diaz-Pace, J. A., Monteserin, A., Demazeau, Y., & Adam, C. (2016). A MAS Approach for Group Recommendation Based on Negotiation Techniques. Lecture Notes in Computer Science, 219-231. doi:10.1007/978-3-319-39324-7_19Rincon, J. A., de la Prieta, F., Zanardini, D., Julian, V., & Carrascosa, C. (2017). Influencing over people with a social emotional model. Neurocomputing, 231, 47-54. doi:10.1016/j.neucom.2016.03.107Aguado, G., Julian, V., Garcia-Fornes, A., & Espinosa, A. (2020). A Multi-Agent System for guiding users in on-line social environments. Engineering Applications of Artificial Intelligence, 94, 103740. doi:10.1016/j.engappai.2020.103740Aguado, G., Julián, V., García-Fornes, A., & Espinosa, A. (2020). Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks. Applied Sciences, 10(11), 3754. doi:10.3390/app10113754Camara, M., Bonham-Carter, O., & Jumadinova, J. (2015). A multi-agent system with reinforcement learning agents for biomedical text mining. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics. doi:10.1145/2808719.2812596Lombardo, G., Fornacciari, P., Mordonini, M., Tomaiuolo, M., & Poggi, A. (2019). A Multi-Agent Architecture for Data Analysis. Future Internet, 11(2), 49. doi:10.3390/fi11020049Schweitzer, F., & Garcia, D. (2010). An agent-based model of collective emotions in online communities. The European Physical Journal B, 77(4), 533-545. doi:10.1140/epjb/e2010-00292-