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Automated System for Detecting Mental Stress of Users in Social Networks Using Data Mining Techniques

  • Shraddha Sharma
  • Ila Sharma
  • A. K. SharmaEmail author
Conference paper
  • 43 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

This paper provides an automatic system for detecting mental stress of users in social networks and data mining algorithms have been applied here. Data mining is usually defined as the extraction of non-trivial implicit that are unknown previously and the most valuable information present in the data. It is commonly known as the knowledge discovery from the databases(KDD). In data mining, on examining data for recurrent then/if forms association rules could be formed through consuming Confidence & Support measures to detect most significant associations in the data. Support is exactly how regularly the items perform in the folder, while self-assurance is the sum of times then/if declarations are precise. In this automated system, firstly a set of stress-related textual, visual, and social attributes from various aspects are evaluated. We detected the correlation between the states of user’s stress and their social interaction behavior in social networks by utilizing real world social media data. In this work, evaluated polarity of sentiments from social media data to identify phenomena of stress among the users. The proposed methodology provides the best performance results, when it compared to the existing methods.

Keywords

Data mining Stress Social media Tweets Sentiments 

References

  1. 1.
    World Health Organization: The World Health Report 2001, vol. 36, no. 10. WHO (2001)Google Scholar
  2. 2.
    Global Burden of Disease Study 2013 Collaborators: Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013, Lancet, vol. 386, no. 9995, pp. 743–800 (2015)Google Scholar
  3. 3.
    Institute for Public Health (IPH) 2017: National Health and Morbidity Survey 2017 (NHMS 2017): Adolescent Mental Health (DASS-21) (2011)Google Scholar
  4. 4.
    Pillai, R.G., Thelwall, M., Orasan, C.: Detection of stress and relaxation magnitudes for Tweets. In: International World Wide Web Conference Committee ACM (2018)Google Scholar
  5. 5.
    Tavana, M., Abtahi, A.-R., Di Caprio, D., Poortarigh, M.: An artificial neural network and bayesian network model for liquidity risk assessment in banking. Neurocomputing 275, 2525–2554 (2018)CrossRefGoogle Scholar
  6. 6.
    Khanchouch, I., Limam, M.: Adapting a multi-SOM clustering algorithm to large banking data. In: World Conference on Information Systems and Technologies, pp. 171–181 (2018)Google Scholar
  7. 7.
    Calis, A., Boyaci, A., Baynal, K.: Data mining application in banking sector with clustering and classification methods. In: 2015 International Conference on Industrial Engineering and Operations Management (IEOM), pp. 1–8 (2015)Google Scholar
  8. 8.
    Chitra, K., Subashini, B.: Data mining techniques and its applications in banking sector. Int. J. Emerg. Technol. Adv. Eng. 3, 219–226 (2013)Google Scholar
  9. 9.
    Babaie, S.S.: Implementation of two stages k-means algorithm to apply a payment system provider framework in banking systems. In: Artificial Intelligence Perspectives and Applications, pp. 203–213. Springer (2015)Google Scholar
  10. 10.
    Zhao, J., Gui, X.: Comparison research on text pre-processing methods on Twitter sentiment analysis. IEEE Access 5, 2870–2879 (2017)CrossRefGoogle Scholar
  11. 11.
    Singh, T., Kumari, M.: Role of text pre-processing in Twitter sentiment analysis. Proc. Comput. Sci. 89, 549–554 (2016)CrossRefGoogle Scholar
  12. 12.
    Kepios: Digital in 2018, essential insights into internet, social media, mobile, and ecommerce use around the world, April 2018. https://kepios.com/data
  13. 13.
    Marechal, C., et al.: Survey on AI-based multimodal methods for emotion detection. Springer LNCS 11400, pp. 307–324 (2019).  https://doi.org/10.1007/978-3-030-16272-6_11Google Scholar
  14. 14.
    Sundarkumar, G.G., Ravi, V.: A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance. Eng. Appl. Artif. Intell. 37, 368–377 (2015)CrossRefGoogle Scholar
  15. 15.
    Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)CrossRefGoogle Scholar
  16. 16.
    Luo, C., Pang, W., Wang, Z., Lin, C.: Hete-CF: social-based collaborative filtering recommendation using heterogeneous relations. In: 2014 IEEE International Conference on Data Mining (ICDM), pp. 917–922 (2014)Google Scholar
  17. 17.
    Tyagi, E., Sharma, A.K.: Sentiment analysis of product reviews using support vector machine learning algorithm. Ind. J. Sci. Technol. 10(35), 1–9 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.R.N. Modi Engineering CollegeKotaIndia
  2. 2.Department of CSER.N. Modi Engineering CollegeKotaIndia
  3. 3.CSIKota UniversityKotaIndia

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