Knowledge discovery applying text mining techniques in Psychology

Article Sidebar

PDF (Español (España)) ID AUTORES (Español (España))
Published: Aug 9, 2017
DOI: https://doi.org/10.32348/1852.4206.v9.n2.12701
Keywords:
text mining techniques, computer sciences, assessment, Psychology

Main Article Content

Luciana Mariñelarena-Dondena
Universidad Nacional de San Luis. Laboratorio de Investigación y Desarrollo en Inteligencia Computacional
Marcelo Luis Errecalde
Universidad Nacional de San Luis. Laboratorio de Investigación y Desarrollo en Inteligencia Computacional
Alejandro Castro Solano
Universidad de Palermo

Abstract

The knowledge discovery in databases (KDD) is concerned with the non-trivial process of making sense of data. Data mining is only a step in the KDD process that consists in pattern recognition using statistics and machine learning techniques. This literature review focuses on how text mining techniques can be applied in Psychology. In this context, the two main purposes of text mining techniques will be introduced: description and prediction. Finally, this paper highlights the use of text mining techniques as a psychological assessment tool, which differs from the use of standard questionnaires or scales.

Downloads

Download data is not yet available.

Article Details

How to Cite
Mariñelarena-Dondena, L., Errecalde, M. L., & Castro Solano, A. (2017). Knowledge discovery applying text mining techniques in Psychology. Argentinean Journal of Behavioral Sciences, 9(2), 65–76. https://doi.org/10.32348/1852.4206.v9.n2.12701
Issue
Vol. 9 No. 2 (2017): Revista Argentina de Ciencias del Comportamiento
Section
Technical or Methodological Articles

La RACC aplicará la licencia internacional de atribuciones comunes creativas (Reconocimiento 4.0 Internacional: https://creativecommons.org/licenses/by/4.0/).

Bajo esta licencia, se permite cualquier explotación de la obra, incluyendo la explotación con fines comerciales y la creación de obras derivadas, la distribución de las cuales también está permitida sin ninguna restricción. Esta licencia es una licencia libre según la Freedom Defined. La única condición es que siempre y en todos los casos se cite a los autores y a la fuente original de publicación (i.e., RACC). Esta licencia fue desarrollada para facilitar el acceso abierto, gratuito y libre a trabajos originales científicos y artísticos.

Author Biographies

Luciana Mariñelarena-Dondena, Universidad Nacional de San Luis. Laboratorio de Investigación y Desarrollo en Inteligencia Computacional

Consejo Nacional de Investigaciones Científicas y Técnicas.

Alejandro Castro Solano, Universidad de Palermo

Consejo Nacional de Investigaciones Científicas y Técnicas.

References

Bellmore, A., Calvin, A. J., Xu, J. M., & Zhu, X. (2015). The five W’s of ‘‘bullying’’ on Twitter: Who, What, Why, Where, and When. Computers in Human Behavior, 44, 305–314.

Conover, M. D., Gonçalves, B., Ratkiewicz, J., Flammini, A., & Menczer, F. (2011). Predicting the Political Alignment of Twitter Users. Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom). Boston, USA, 192-199. doi: 10.1109/PASSAT/SocialCom.2011.34

Desmet, B., & Hoste, V. (2013). Emotion detection in suicide notes. Expert Systems with Applications, 40(16), 6351–6358.

Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., … Seligman, M. E. P. (2015). Psychological

Language on Twitter Predicts County-Level Heart Disease Mortality. Psychological Science, 26(2), 159-169. doi: 10.1177/0956797614557867

Eftekhar, A., Fullwood, C., & Morris, N. (2014). Capturing personality from Facebook photos and photo-related activities: How much exposure do you need? Computers in Human Behavior, 37, 162–170.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

Hernández Orallo, J., Ramírez Quintana, M. J., & Ferri Ramírez, C. (2004). Introducción a la Minería de Datos. Madrid: Pearson Prentice Hall.

Jim Wu, Y. C., Chang, W. H., & Yuan, C. H. (2015). Do Facebook profile pictures reflect user’s personality? Computers in Human Behavior, 51(B), 880-889.

Joachims, T. (1998) Text categorization with Support Vector Machines: Learning with many relevant features. En C. Nédellec & C. Rouveirol (Eds.), Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) (pp. 137-142). Heidelberg: Springer.

Joachims, T. (1999). Transductive inference for text classification using support vector machines. En I. Bratko & S. Dzeroski (Eds.), Proceedings of ICML-99, 16th International Conference on Machine Learning (pp. 200–209). San Francisco: Morgan Kaufmann

Publishers.

Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016). Gaining Insights From Social Media Language: Methodologies and Challenges. Psychological Methods, 21(4), 507-525.

Laney, D. (2001). 3-D data management: Controlling data volume, velocity and variety. Application Delivery Strategies, META Group Inc. Recuperado de http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-

Velocity-and-Variety.pdf

Layton, R., Watters, P., & Dazeley, R. (2011). Recentred Local Profiles for Authorship Attribution. Natural Language Engineering, 18(3), 293-312.

Lex, E. (2011). Content Facets for Individual Information Needs in Media. (Tesis Doctoral). Graz University of Technology, Styria, Austria.

Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007). Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. Journal of Artificial Intelligence Research, 30, 457-500.

McCallum, A., & Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification. Learning for Text Categorization: Papers from the 1998 AAAI Workshop, 752, 41-48.

Mitchell, T. M. (1996). Machine Learning. New York: McGraw Hill.

Pennebaker, J. W. (2002). What our words can say about us: Toward a broader language psychology. Psychological Science Agenda, 15(1), 8-9.

Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). New Jersey: Prentice Hall.

Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Lucas, R. E., Agrawal, M., … Ungar, L. H. (2013). Characterizing Geographic Variation in Well-Being using Tweets. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (ICWSM), Boston, USA, 583-591

Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., … Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLOS ONE, 8(9), e73791.

Schwartz, H. A., & Ungar, L. H. (2015). Data-Driven Content Analysis of Social Media: A Systematic Overview of Automated Methods. The ANNALS of the American Academy of Political and Social Science, 659(1), 78-94.

Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1), 1-47.

Tausczik, Y. R., & Pennebaker, J. W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24-54.