Classificação de personalidade no Twitter: Uma análise sobre a viabilidade de transferência multicultural de aprendizagem
Resumo
Extrair informações sobre personalidade é objeto de estudo de pesquisas com diversas aplicações, como sistemas de recomendação, processos de recrutamento, entre outras. Esta área de pesquisa possui recursos e metodologias bem estabelecidas e eficientes, porém com alta concentração de aplicações em dados provenientes da língua inglesa. Este artigo contribui na classificação de personalidade para línguas que dispõem de um número menor de recursos, a partir da utilização de técnicas voltadas para a língua inglesa, assim, permitindo que os avanços de tecnologias de classificação de personalidade criados para o público falante do inglês reflitam no avanço de outras línguas e culturas. Para isto, são utilizadas técnicas de Mineração de Dados, Processamento de Linguagem Natural e Machine Learning com Word Embedding, explorando a correlação entre traços de personalidade e propriedades léxicas textuais, com dados oriundos de Redes Sociais Online. Os resultados obtidos são satisfatórios quando comparados com pesquisas correlatas aplicadas apenas na língua inglesa, assim, demonstrando a viabilidade de utilização mais ampla de técnicas antes voltadas a uma única cultura, indicando a possibilidade de superar a barreira multicultural presente na literatura.
Palavras-chave:
Personalidade, Machine Learning, Processamento de Linguagem Natural, Word Embedding
Referências
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Celli, F., Pianesi, F., Stillwell, D., and Kosinski, M. (2013). Workshop on computational personality recognition: Shared task. InProceedings of the International AAAI Conference on Web and Social Media, volume 7.
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Grave, E., Bojanowski, P., Gupta, P., Joulin, A., and Mikolov, T. (2018). Learning word vectors for 157 languages. InProceedings of the International Conference on Language Resources and Evaluation (LREC 2018)
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Johnson, J. A. (2014). Measuring thirty facets of the five factor model with a 120 itempublic domain inventory: Development of the ipip-neo-120.Journal of Research in Personality, 51:78–89.
Kenney, J. F. and Keeping, E. (1962). Linear regression and correlation.Mathematics ofstatistics, 1:252–285
Lopes, M. C. S. (2004). Mineração de dados textuais utilizando técnicas de clustering para o idioma português.Rio de Janeiro: sn
Mairesse, F., Walker, M. A., Mehl, M. R., and 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
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space.arXiv preprint arXiv:1301.3781.
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Pennebaker, J. W., Francis, M. E., and Booth, R. J. (2001). Linguistic inquiry and word count: Liwc 2001.Mahway: Lawrence Erlbaum Associates, 71(2001):2001
Quercia, D., Kosinski, M., Stillwell, D., and Crowcroft, J. (2011). Our twitter profiles, ourselves: Predicting personality with twitter. In2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, pages 180–185. IEEE
Rasmussen, C. E. (2006). Gaussian processes for machine learning. MIT Press
Refaeilzadeh, P., Tang, L., and Liu, H. (2009).Cross-Validation, pages 532–538. Springer US, Boston, MA
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M.,Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E., et al. (2013).Personality, gender, and age in the language of social media: The open-vocabulary approach.PloS one, 8(9):e73791
Seidman, G. (2013). Self-presentation and belonging on facebook: How personality influences social media use and motivations.Personality and individual differences,54(3):402–407.
Shevade, S., Keerthi, S., Bhattacharyya, C., and Murthy, K. (1999). Improvements to the smo algorithm for svm regression. In IEEE Transactions on Neural Networks.
Tankovska, H. (2021). Social media - statistics & facts. Acessado em 12/04/2021.
Witten, I. H., Frank, E., Hall, M., and Pal, C. (2016). The weka workbench. online appendix for “data mining: Practical machine learning tools and techniques”. In Morgan Kaufmann. Fourth Edition, 2016.
Bayram, N. and Aydemir, M. (2017). Decision-making styles and personality traits.
Bird, S., Klein, E., and Loper, E. (2009).Natural Language Processing with Python.O’Reilly Media
Carducci, G., Rizzo, G., Monti, D., Palumbo, E., and Morisio, M. (2018). Twitpersonality: Computing personality traits from tweets using word embeddings and supervised learning.Information, 9(5):127
Celli, F., Pianesi, F., Stillwell, D., and Kosinski, M. (2013). Workshop on computational personality recognition: Shared task. InProceedings of the International AAAI Conference on Web and Social Media, volume 7.
Coltheart, M. (1981). The mrc psycholinguistic database.The Quarterly Journal of Experimental Psychology Section A, 33(4):497–505
Costa, P. and Mccrae, R. (1992). Neo pi-r professional manual.Psychological Assessment Resources, 396.
dos Santos, W. R., Ramos, R. M., and Paraboni, I. (2019). Computational personality recognition from facebook text: psycholinguistic features, words and facets.New Review of Hypermedia and Multimedia, 25(4):268–287
Enge, j. M., G ̊asodden, G., Morten Amundsen, O., Sundt, P. A., and Moxnes, M.(2020). Big five personality test.https://bigfive-test.com/. Acessado em 01/12/2020
Goldberg, L. R. (1990). An alternative ”description of personality”: the big-five factor structure.Journal of personality and social psychology, 59 6:1216–29
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., and Mikolov, T. (2018). Learning word vectors for 157 languages. InProceedings of the International Conference on Language Resources and Evaluation (LREC 2018)
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., and Scholkopf, B. (1998). Support vector machines.IEEE Intelligent Systems and their Applications, 13(4):18–28
acobelli, F., Gill, A. J., Nowson, S., and Oberlander, J. (2011). Large scale personality classification of bloggers. Ininternational conference on affective computing and intelligent interaction, pages 568–577. Springer
John, O. P., Srivastava, S., et al. (1999). The big five trait taxonomy: History, measurement, and theoretical perspectives.Handbook of personality: Theory and research,2(1999):102–138.
Johnson, J. A. (2014). Measuring thirty facets of the five factor model with a 120 itempublic domain inventory: Development of the ipip-neo-120.Journal of Research in Personality, 51:78–89.
Kenney, J. F. and Keeping, E. (1962). Linear regression and correlation.Mathematics ofstatistics, 1:252–285
Lopes, M. C. S. (2004). Mineração de dados textuais utilizando técnicas de clustering para o idioma português.Rio de Janeiro: sn
Mairesse, F., Walker, M. A., Mehl, M. R., and 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
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space.arXiv preprint arXiv:1301.3781.
Newberry, C. (2021). 36 twitter stats all marketers need to know in 2021. Acessado em12/04/2021
Noguchi, K., Gohm, C. L., and Dalsky, D. (2006). Cognitive tendencies of focusing on positive and negative information
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
Pennebaker, J. W., Francis, M. E., and Booth, R. J. (2001). Linguistic inquiry and word count: Liwc 2001.Mahway: Lawrence Erlbaum Associates, 71(2001):2001
Quercia, D., Kosinski, M., Stillwell, D., and Crowcroft, J. (2011). Our twitter profiles, ourselves: Predicting personality with twitter. In2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, pages 180–185. IEEE
Rasmussen, C. E. (2006). Gaussian processes for machine learning. MIT Press
Refaeilzadeh, P., Tang, L., and Liu, H. (2009).Cross-Validation, pages 532–538. Springer US, Boston, MA
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M.,Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E., et al. (2013).Personality, gender, and age in the language of social media: The open-vocabulary approach.PloS one, 8(9):e73791
Seidman, G. (2013). Self-presentation and belonging on facebook: How personality influences social media use and motivations.Personality and individual differences,54(3):402–407.
Shevade, S., Keerthi, S., Bhattacharyya, C., and Murthy, K. (1999). Improvements to the smo algorithm for svm regression. In IEEE Transactions on Neural Networks.
Tankovska, H. (2021). Social media - statistics & facts. Acessado em 12/04/2021.
Witten, I. H., Frank, E., Hall, M., and Pal, C. (2016). The weka workbench. online appendix for “data mining: Practical machine learning tools and techniques”. In Morgan Kaufmann. Fourth Edition, 2016.
Publicado
18/07/2021
Como Citar
OLIVEIRA, Arthur Pereira de; SERUFFO, Marcos César da Rocha.
Classificação de personalidade no Twitter: Uma análise sobre a viabilidade de transferência multicultural de aprendizagem. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 10. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 67-78.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2021.16126.
