IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v9y2017i2p22-d101646.html

A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

Author

Listed:
  • Weijun Wang

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China)

  • Ying Li

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
    School of Information Management, Central China Normal University, Wuhan 430079, China)

  • Yinghui Huang

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
    School of Information Management, Central China Normal University, Wuhan 430079, China)

  • Hui Liu

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
    School of Information Management, Central China Normal University, Wuhan 430079, China)

  • Tingting Zhang

    (Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
    School of Information Management, Central China Normal University, Wuhan 430079, China)

Abstract

Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users’ real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users’ real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users’ online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users’ real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users’ real mood states and could be applied in further psychological measurements and predictions.

Suggested Citation

  • Weijun Wang & Ying Li & Yinghui Huang & Hui Liu & Tingting Zhang, 2017. "A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics," Future Internet, MDPI, vol. 9(2), pages 1-13, June.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:2:p:22-:d:101646
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/9/2/22/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/9/2/22/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    2. Gardner, Meryl Paula, 1985. "Mood States and Consumer Behavior: A Critical Review," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(3), pages 281-300, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Breuer, Christoph & Boronczyk, Felix & Rumpf, Christopher, 2021. "Message personalization and real-time adaptation as next innovations in sport sponsorship management? How run-of-play and team affiliation affect viewer response," Journal of Business Research, Elsevier, vol. 133(C), pages 309-316.
    2. Kalamas, Maria & Laroche, Michel & Makdessian, Lucy, 2008. "Reaching the boiling point: Consumers' negative affective reactions to firm-attributed service failures," Journal of Business Research, Elsevier, vol. 61(8), pages 813-824, August.
    3. Ahmed Bouteska & Murad Harasheh, 2026. "Decoding the crypto crowd: how social media sentiment predicts Ethereum’s price," Journal of Asset Management, Palgrave Macmillan, vol. 27(1), pages 1-12, March.
    4. Darko Cherepnalkoski & Andreas Karpf & Igor Mozetič & Miha Grčar, 2016. "Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-27, November.
    5. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    6. Drichoutis, Andreas & Nayga, Rodolfo & Klonaris, Stathis, 2010. "The Effects of Induced Mood on Preference Reversals and Bidding Behavior in Experimental Auction Valuation," MPRA Paper 25597, University Library of Munich, Germany.
    7. LEMOINE, Jean-François & PLICHON, Véronique, 2000. "Le rôle des facteurs situationnels dans l'explication des réactions affectives du consommateur à l'intérieur d'un point de vente. / The Role of Situational Factors in the Understanding of Consumer Affective Responses in a Sales Outlet," LEG - CERMAB / Centre de Recherche en Marketing de Bourgogne - Cahier de recherche 2000-04, LEG - CERMAB, CNRS UMR 5118, Université de Bourgogne.
    8. Francesco Petrucci, 2013. "Musical priming effects on food preference," The International Journal of Economic Behavior - IJEB, Faculty of Business and Administration, University of Bucharest, vol. 3(1), pages 155-167, December.
    9. Battisti, Michele & Kauppinen, Ilpo & Rude, Britta, 2024. "Breaking the silence: The effects of online social movements on gender-based violence," European Journal of Political Economy, Elsevier, vol. 85(C).
    10. Vicentini, Giulio & Nucci, Alessandro & Caldarelli, Guido & Omodei, Elisa, 2025. "Social media discussions anticipates financial market volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 661(C).
    11. repec:osf:thesis:6gptv_v1 is not listed on IDEAS
    12. Rizomyliotis, Ioannis & Konstantoulaki, Kleopatra & Kostopoulos, Ioannis, 2018. "Reassessing the effect of colour on attitude and behavioural intentions in promotional activities: The moderating role of mood and involvement," Australasian marketing journal, Elsevier, vol. 26(3), pages 204-215.
    13. Murray, Kyle B. & Di Muro, Fabrizio & Finn, Adam & Popkowski Leszczyc, Peter, 2010. "The effect of weather on consumer spending," Journal of Retailing and Consumer Services, Elsevier, vol. 17(6), pages 512-520.
    14. Muhammad Bilal Gulfraz & Xiong Wei, 2019. "Impact of Sales Person’s Customer Orientation, In-Store Promotions and In-Store Environment on Impulsive Buying Mediated by Urge: The Moderating Role of Impulsive Buying Tendency," International Journal of Science and Business, IJSAB International, vol. 3(6), pages 99-117.
    15. Mun Hwan Kim & Jin Woo Park & Yu Jin Choi, 2020. "A Study on the Effects of Waiting Time for Airport Security Screening Service on Passengers’ Emotional Responses and Airport Image," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    16. Sakariyahu, Rilwan & Lawal, Rodiat & Adigun, Rasheed & Paterson, Audrey & Johan, Sofia, 2024. "One crash, too many: Global uncertainty, sentiment factors and cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 94(C).
    17. Sierra, Jeremy J. & Hyman, Michael R., 2011. "Outlet mall shoppers' intentions to purchase apparel: A dual-process perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 18(4), pages 341-347.
    18. A. K. S. Suryavanshi & Viral Bhatt & Sujo Thomas & Ritesh Patel & Twinkle Trivedi, 2024. "Predicting CRM purchase intention by examining congruence, shopping orientation and mood moderation: Applying congruence theory with SEM-ANN-NCA method," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 21(3), pages 565-594, September.
    19. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    20. Marc-Aurèle Divernois & Damir Filipović, 2024. "StockTwits classified sentiment and stock returns," Digital Finance, Springer, vol. 6(2), pages 249-281, June.
    21. Chen, Long & Huang, Jiahui & Jing, Peng & Wang, Bichen & Yu, Xiaozhou & Zha, Ye & Jiang, Chengxi, 2023. "Changing or unchanging Chinese attitudes toward ride-hailing? A social media analytics perspective from 2018 to 2021," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:9:y:2017:i:2:p:22-:d:101646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.