Human Values Classification in Social Network Using Machine Learning
Abstract
Twitter receives millions of messages daily from different users and regions of the world. These messages contain opinions, evaluations, and feelings, making Twitter a suitable platform for various types of study. Thus, we can use this social network to discover important information about people, for instance, emotional state, their opinions about products, political preference, etc. This work aims to create and evaluate models for classification of human values, a psychology concept that defines the guide principles of people. We tested three machine learning classifiers, trained using Twitter messages, to predict the answers of a questionnaire used by psychologists to determine human values. The best result reached 60,08% of accuracy.
Keywords:
human values, machine learning, social network
References
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Gouveia, V. V. (2013). Teoria funcionalista dos valores humanos: fundamentos, aplicações e perspectivas [functional theory of human values: Fundamentals, applications, and perspectives]. São Paulo: Casa do Psicólogo.
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Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Japkowicz, N. and Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent data analysis, 6(5):429–449.
John, O. P., Donahue, E. M., and Kentle, R. L. (1991). The big ve inventory-versions 4a and 54.
Mani, I. and Zhang, I. (2003). knn approach to unbalanced data distributions: a case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets, volume 126.
Medeiros, E. D. d. et al. (2011). Teoria funcionalista dos valores humanos: Testando sua adequação intra e interculturalmente.
Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings. The Journal of Abnormal and Social Psychology, 66(6):574.
Pak, A. and Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In LREc, volume 10, pages 1320–1326.
Paulhus, D. L. and Williams, K. M. (2002). The dark triad of personality: Narcissism, machiavellianism, and psychopathy. Journal of research in personality, 36(6):556– 563.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Qiu, L., Lin, H., Ramsay, J., and Yang, F. (2012). You are what you tweet: Personality expression and perception on twitter. Journal of Research in Personality, 46(6):710– 718.
Sumner, C., Byers, A., Boochever, R., and Park, G. J. (2012). Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. In 2012 11th International Conference on Machine Learning and Applications, volume 2, pages 386–393. IEEE.
Twitter (2018). Quarterly results - about. https://investor.twitterinc.com/results.cfm.
Published
2019-10-15
How to Cite
CORREIA, Sérgio; MALHEIROS, Yuri; NASCIMENTO, Anderson do; GOUVEIA, Valdiney.
Human Values Classification in Social Network Using Machine Learning. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 37-48.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9270.
