An Efficient Analysis of Psychological Stress Prediction Technique Using Social Interaction of Social Networks

Authors(2) :-G. Prashanti, Mujafar Abdul Ghani

Mental general despondency is weakening people's prosperity. It is non-immaterial to perceive push propitious for proactive care. With the reputation of online organizing, people are familiar with offering their step by step activities and working together to associates through electronic systems administration media stages, making it conceivable to utilize online relational association data for extend ID. In this paper, we find that customers push state is about related to that of his/her mates in web-based systems administration, and we use a tremendous scale dataset from certifiable social stages to systematically mull over the association of customers' tension states and social co-activities. We at first describe a plan of pressure related artistic, visual, and social characteristics from various points and after that propose a novel cream show - a factor chart show joined with Con-volition Neural System to utilize tweet substance and social affiliation information for extending area. Test happens exhibit that the proposed model can upgrade the area execution by 6-9% in F1-score. By furthermore separating the social affiliation data, we moreover locate a couple of spellbinding wonders, i.e. the amount of social structures of sparse affiliations (i.e. with no delta relationship) of centered customers is around 14% higher than that of non-concentrated on customers, exhibiting that the social structure of concentrated on customers' mates tend to be less related and less jumbled than that of non-concentrated on customers.

Authors and Affiliations

G. Prashanti
MCA Department, Vignan's Lara Institute of Technology and Science, Vadlamudi, Guntur, Andhra Pradesh, India
Mujafar Abdul Ghani

Stress detection, factor graph model, micro-blog, social media, healthcare, social interaction.

  1. Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi, and Alex Pentland. Daily stress recognition from mobile phone data, weather conditions and individual traits. In ACM Interna-tional Conference on Multimedia, pages 477–486, 2014.
  2. Chris Buckley and Ellen M Voorhees. Retrieval evaluation with in-complete information. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 25–32, 2004.
  3. Xiaojun Chang, Yi Yang, Alexander G Hauptmann, Eric P Xing, and Yao-Liang Yu. Semantic concept discovery for large-scale zero-shot event detection. In Proceedings of International Joint Conference on Artificial Intelligence, pages 2234–2240, 2015.
  4. Wanxiang Che, Zhenghua Li, and Ting Liu. Ltp: A chinese language technology platform. In Proceedings of International Con-ference on Computational Linguistics, pages 13–16, 2010.
  5. Chih chung Chang and Chih-Jen Lin. Libsvm: a library for sup-port vector machines. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2(3):389–396, 2001.
  6. Dan C Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gam-bardella, and Jurgen¨ Schmidhuber. Flexible, high performance convolutional neural networks for image classification. In Proceed-ings of International Joint Conference on Artificial Intelligence, pages 1237–1242, 2011.
  7. Sheldon Cohen and Thomas A. W. Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2):310–357, 1985.
  8. Glen Coppersmith, Craig Harman, and Mark Dredze. Measuring post traumatic stress disorder in twitter. In Proceedings of the International Conference on Weblogs and Social Media, pages 579–582, 2014.
  9. Rui Fan, Jichang Zhao, Yan Chen, and Ke Xu. Anger is more influential than joy: Sentiment correlation in weibo. PLoS ONE, 2014.
  10. Zhanpeng Fang, Xinyu Zhou, Jie Tang, Wei Shao, A.C.M. Fong, Longjun Sun, Ying Ding, Ling Zhou, , and Jarder Luo. Modeling paying behavior in game social networks. In In Proceedings of the Twenty-Third Conference on Information and Knowledge Management (CIKM’14), pages 411–420, 2014.
  11. Golnoosh Farnadi, Geetha Sitaraman, Shanu Sushmita, Fabio Celli, Michal Kosinski, David Stillwell, Sergio Davalos, Marie Francine Moens, and Martine De Cock. Computational personality recognition in social media. User Modeling and User-Adapted Interaction, pages 1–34, 2016.
  12. Eileen Fischer and A. Rebecca Reuber. Social interaction via new social media: (how) can interactions on twitter affect effectual thinking and behavior? Journal of Business Venturing, 26(1):1–18, 2011.
  13. Jerome H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5):1189–1232, 1999.
  14. Rui Gao, Bibo Hao, He Li, Yusong Gao, and Tingshao Zhu. Developing simplified chinese psychological linguistic analysis dictionary for microblog. pages 359–368, 2013.
  15. Johannes Gettinger and Sabine T. Koeszegi. More Than Words: The Effect of Emoticons in Electronic Negotiations.
  16. Jennifer Golbeck, Cristina Robles, Michon Edmondson, and Karen Turner. Predicting personality from twitter. In Passat/socialcom 2011, Privacy, Security, Risk and Trust, pages 149–156, 2011.
  17. Mark S. Granovetter. The strength of weak ties. American Journal of Sociology, 1973.
  18. Quan Guo, Jia Jia, Guangyao Shen, Lei Zhang, Lianhong Cai, and Zhang Yi. Learning robust uniform features for cross-media social data by using cross autoencoders. Knowledge Based System, 102:64– 75, 2016.
  19. David W. Hosmer, Stanley Lemeshow, and Rodney X. Sturdivant. Applied logistic regression. Wiley series in probability and mathemat-ical statistics, 2013.
  20. Sung Ju Hwang. Discriminative object categorization with exter-nal semantic knowledge. 2013.
  21. Sepandar D. Kamvar. We feel fine and searching the emotional web. In In Proceedings of WSDM, pages 117–126, 2011.
  22. Herbert C. Kelman. Compliance, identification, and internal-ization: Three processes of attitude change. general information, 1(1):51–60, 1958.
  23. Shigenobu Kobayashi. The aim and method of the color image scale. Color research & application, 6(2):93–107, 1981.
  24. Novak P Kralj, J Smailovi, B Sluban, and I Mozeti. Sentiment of emojis. Plos One, 10(12), 2015.
  25. Frank R Kschischang, Brendan J Frey, and H-A Loeliger. Factor graphs and the sum-product algorithm. Information Theory, IEEE Transactions on, 47(2):498–519, 2001.
  26. Yann LeCun and Yoshua Bengio. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361, 1995.
  27. Kathy Lee, Ankit Agrawal, and Alok Choudhary. Real-time disease surveillance using twitter data: demonstration on flu and cancer. In Proceedings of ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1474–1477, 2013.
  28. H. Lin, J. Jia, Q. Guo, Y. Xue, J. Huang, L. Cai, and L. Feng. Psy-chological stress detection from cross-media microblog data using deep sparse neural network. In proceedings of IEEE International Conference on Multimedia & Expo, 2014.
  29. H. Lin, J. Jia, Q. Guo, Y. Xue, Q. Li, J Huang, L. Cai, and L. Feng. User-level psychological stress detection from social media using deep neural network. In Proceedings of ACM Int. Conference on Multimedia, 2014.
  30. Li Liu and Ling Shao. Learning discriminative representations from rgb-d video data. In Proceedings of International Joint Confer-ence on Artificial Intelligence, pages 1493–1500, 2013.
  31. H-A Loeliger. An introduction to factor graphs. Signal Processing Magazine, IEEE, 21(1):28–41, 2004.
  32. Jana Machajdik and Allan Hanbury. Affective image classification using features inspired by psychology and art theory. In Proceed-ings of the international conference on Multimedia, pages 83–92, 2010.
  33. Kevin P Murphy, Yair Weiss, and Michael I Jordan. Loopy belief propagation for approximate inference: An empirical study. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pages 467–475, 1999.
  34. Cristian Danescu niculescu mizil, Lillian Lee, Bo Pang, and Jon Kleinberg. Echoes of power: Language effects and power differ-ences in social interaction. eprint arXiv:1112.3670, 2011.
  35. Liqiang Nie, Yi-Liang Zhao, Mohammad Akbari, Jialie Shen, and Tat-Seng Chua. Bridging the vocabulary gap between health seek-ers and healthcare knowledge. Knowledge and Data Engineering, IEEE Transactions on, 27(2):396–409, 2015.
  36. Federico Alberto Pozzi, Daniele Maccagnola, Elisabetta Fersini, and Enza Messina. Enhance user-level sentiment analysis on microblogs with approval relations. In AI* IA 2013: Advances in Artificial Intelligence, pages 133–144. 2013.
  37. Neumann R and Strack F. ”mood contagion”: the automatic transfer of mood between persons. Journal of Personality and Social Psychology, pages 211–223, 2000.
  38. Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. User-level sentiment analysis incorporating social networks. In Proceedings of the SIGKDD international conference on Knowledge discovery and data mining, pages 1397–1405, 2011.
  39. Wenbin Tang, Honglei Zhuang, and Jie Tang. Learning to infer social ties in large networks. In Machine Learning and Knowledge Discovery in Databases, pages 381–397. 2011.
  40. Y. R. Tausczik and J. W. Pennebaker. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24–54, 2010.
  41. Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, and Arvid Kappas. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12):2544–2558, 2010.
  42. Svetnik V. Random forest: a classification and regression tool for compound classification and qsar modeling. Journal of Chemical Information and Computer Sciences, 43(6):1947–1958, 2003.
  43. Ben Verhoeven, Walter Daelemans, and Barbara Plank. Twisty: A multilingual twitter stylometry corpus for gender and personality profiling. In Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC, pages 1632–1637, 2016.

Publication Details

Published in : Volume 4 | Issue 2 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 87-94
Manuscript Number : CSEIT1833619
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

G. Prashanti, Mujafar Abdul Ghani, "An Efficient Analysis of Psychological Stress Prediction Technique Using Social Interaction of Social Networks ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 2, pp.87-94, March-April-2018.
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