Using Machine Learning for Sentiment and Social Influence Analysis in Text

  • Emmanuel Awuni Kolog
  • Calkin Suero Montero
  • Tapani Toivonen
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)

Abstract

Students’ academic achievement is largely driven by their social phenomena, which is shaped by social influence and opinion dynamics. In this paper, we employed a machine learning technique to detect social influence and sentiment in text-based students’ life stories. The life stories were first pre-processed and clustered using k-means with euclidean distance. After that, we identified domestic, peer and school staff as the main influences on students’ academic development. The various influences were used as class labels for supervised classification using SMO, MNB and J48 decision tree classifiers. In addition, the stories were manually labelled with positive and negative sentiments. We employed 10-folds cross-validation in classifying the sentiments and the social influences in the story corpus. The result show that peer influence is more salient on students’ academic development followed by staff (15%) and domestic influences (12%). However, the remaining 54% of the stories contains unrelated social and other influences. Also, Students expressed more negative sentiment towards academic engagement than the positive sentiments. As per the classifier performance, SMO was found to be superior over MNB and J48 in the sentiment classification while MNB also performed slightly better than the SMO and J48 in the social influence analysis.

Keywords

Clustering Sentiment Social influence Text classification Student 
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References

  1. 1.
    Moussaïd, M., Kämmer, J.E., Analytis, P.P., Neth, H.: Social influence and the collective dynamics of opinion formation. PloS one 8(11), e78433 (2013)CrossRefGoogle Scholar
  2. 2.
    Fiske, S.T.: Social Beings: Core Motives in Social Psychology. Wiley, Hoboken (2009)Google Scholar
  3. 3.
    Ganotice, F.A., King, R.B.: Social influences on students’ academic engagement and science achievement. Psychol. Stud. 59(1), 30–35 (2014)CrossRefGoogle Scholar
  4. 4.
    Chevalier, M.E.: Teacher-learner relationships in adult education classrooms: the social construction of trust. Unpublished doctoral dissertation (1995)Google Scholar
  5. 5.
    Kolog, E.A., Sutinen, E., Vanhalakka-Ruoho, M.: E-counselling implementation: students’ life stories and counselling technologies in perspective. Int. J. Educ. Dev. Inform. Commun. Technol. 10(3), 32 (2014)Google Scholar
  6. 6.
    Glasheen, K., Campbell, M., Shochet, I.: Opportunities and challenges: school guidance counsellors’ perceptions of counselling students online. Aust. J. Guidance Counselling 23, 222–235 (2013)CrossRefGoogle Scholar
  7. 7.
    Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: models, extensions and external effects. In: Participatory Sensing, Opinions and Collective Awareness, pp. 363–401. Springer International Publishing (2017)Google Scholar
  8. 8.
    Munoz, A.: Machine Learning and Optimization (2014). https://www.cims.nyu.edu/~munoz/files/ml_optimization.pdf. Accessed 02 Mar 2016
  9. 9.
    Lopes, L.A., Machado, V. P., Rabelo, R.D.A.: Automatic cluster labeling through Artificial Neural Networks. In: International Joint Conference on Neural Networks(IJCNN), pp. 762–769 (2014)Google Scholar
  10. 10.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)Google Scholar
  11. 11.
    Altrabsheh, N., Gaber, M., Cocea, M.: SA-E: sentiment analysis for education. In: International Conference on Intelligent Decision Technologies, vol. 255, pp. 353–362, June 2013Google Scholar
  12. 12.
    Kolog, E.A., Sutinen, E., Vanhalakka-Ruoho, M., Suhonen, J., Anohah, E.: Using unified theory of acceptance and use of technology model to predict students’ behavioral intention to adopt and use E-counseling in Ghana. Int. J. Modern Educ. Comput. Sci. 7(11), 1 (2015)CrossRefGoogle Scholar
  13. 13.
    Anderman, L.H., Anderman, E.M.: Social predictors of changes in students’ achievement goal orientations. Contemp. Educ. Psychol. 24(10), 21–37 (1999)CrossRefGoogle Scholar
  14. 14.
    Hughes, J., Kwok, O.M.: Influence of student-teacher and parent-teacher relationships on lower achieving readers’ engagement and achievement in the primary grades. J. Educ. Psychol. 99(1), 39 (2007)CrossRefGoogle Scholar
  15. 15.
    Kindermann, T.: Peer group influences on students’ academic motivation. In: Handbook of Social Influences in School Contexts: Social-Emotional, Motivation and Cognitive Outcomes, pp. 31–47 (2016)Google Scholar
  16. 16.
    Kolog, E.A., Suero Montero, C., Sutinen, E.: Annotation agreement of emotions in text: the influence of counsellors’ emotional state on their emotion perception. In: Proceedings of Advanced Learning Technologies (ICALT), pp. 357–359 (2016)Google Scholar
  17. 17.
    Hughes, J., Kwok, O.M.: Influence of student-teacher and parent-teacher relationships on lower achieving readers’ engagement and achievement in the primary grades. J. Educ. Psychol. 99(1), 39 (2007)CrossRefGoogle Scholar
  18. 18.
    Plutchik, R.: Emotion: Theory, Research, and Experience: Theories of Emotion, vol. 1, p. 399. Academic, New York (1980)Google Scholar
  19. 19.
    Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)CrossRefGoogle Scholar
  20. 20.
    Kolog, E.A., Sutinen, E., Suhonen, J., Anohah, E., Vanhalakka-Ruoho, M.: Towards students’ behavioural intention to adopt and use E-counseling: an empirical approach of using Unified Theory of Acceptance and Use of Technology model. In: IEEE AFRICON, pp. 1–6 (2015)Google Scholar
  21. 21.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput. Appl. Mathematics. 20, 53–65 (1987)CrossRefMATHGoogle Scholar
  22. 22.
    Kaur, D. A Comparative Study of various Distance Measures for Software fault prediction. arXiv preprint arXiv:1411.7474 (2014)
  23. 23.
    Hall, M., Frank, E., Holmes, G.: Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations (2009)Google Scholar
  24. 24.
    Considine, G., Zappalà, G.: Factors influencing the educational performance of students from disadvantaged backgrounds. In: Competing Visions: Refereed Proceedings of the National Social Policy Conference, pp. 91–107 (2002)Google Scholar
  25. 25.
    Gültekin, F., Erkan, Z., Tüzüntürk, S.: The effect of group counseling practices on trust building among counseling trainees: from the perspective of social network analysis. Procedia-Social Behav. Sci. 15, 2415–2420 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Emmanuel Awuni Kolog
    • 1
  • Calkin Suero Montero
    • 1
  • Tapani Toivonen
    • 1
  1. 1.School of ComputingUniversity of Eastern FinlandJoensuuFinland

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