Identificação Automática de Postagens Relacionadas ao Uso Através de Modelos de Aprendizagem Profunda
Resumo
Algumas postagens em Sistemas Sociais (SS) podem trazer informações significativas sobre o uso daquele SS, estas chamamos de Postagens Relacionadas ao Uso (PRU). PRU é um conceito importante para a avaliação de sistemas. Existem ferramentas capazes de identificar as PRUs, porém possuem menor precisão quanto as classificações manuais. Tendo em vista este problema, o presente trabalho busca investigar essa deficiência utilizando modelos de aprendizagem profunda para a identificação automática de PRUs nos SS, neste caso a rede social Twitter. Nos testes realizados nosso modelo possuiu alta precisão de classificação quando comparado com outros utilizados para esta tarefa.
Palavras-chave:
Postagens Relacionadas ao Uso, Aprendizagem Profunda, Sistemas Sociais
Referências
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Hedegaard, S. and Simonsen, J. G. (2013). Extracting usability and user experience information from online user reviews. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13, pages 2089–2098, New York, NY, USA. ACM.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Lima, A. M., Silva, P. B., Cruz, L. A., and Mendes, M. S. (2017). Investigating the polarity of user postings in a social system. In International Conference on Social Computing and Social Media, pages 246–257. Springer.
Loper, E. and Bird, S. (2002). Nltk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1, ETMTNLP ’02, pages 63–70, Stroudsburg, PA, USA. Association for Computational Linguistics.
McKinney, W. et al. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, volume 445, pages 51–56. Austin, TX.
Mendes, M. S. (2015). MALTU – um modelo para avaliação da interação em sistemas sociais a partir da linguagem textual do usuário. PhD thesis, Federal University of Ceará.
Mendes, M. S. and Furtado, E. S. (2017). Uux-posts: a tool for extracting and classifying postings related to the use of a system. In Proceedings of the 8th Latin American Conference on Human-Computer Interaction, page 2. ACM.
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. J. Mach. Learn. Res., 12:2825–2830.
Pereira, R., Baranauskas, M. C. C., and da Silva, S. R. P. (2010). Softwares sociais: uma visão orientada a valores. In Proceedings of the IX Symposium on Human Factors in Computing Systems, pages 149–158. Brazilian Computer Society.
Preece, J., Rogers, Y., and Sharp, H. (2015). Interaction design: beyond human-computer interaction. John Wiley & Sons.
Schuster, M. and Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681.
Shaw, D. (1996). Handbook of usability testing: How to plan, design, and conduct effective tests. Journal of the American Society for Information Science, 47(3):258–259. van der Walt, S., Colbert, S. C., and Varoquaux, G. (2011). The numpy array: a structure for efficient numerical computation. CoRR, abs/1102.1523.
van der Walt, S., Colbert, S. C., and Varoquaux, G. (2011). The numpy array: a structure for efficient numerical computation. CoRR, abs/1102.1523.
Chollet, F. et al. (2015). Keras. https://keras.io.
Chunting, Z., Chonglin, S., Zhiyuan, L., and Francis, C. L. (2015). A c-lstm neural network for text classification. arXiv preprint arXiv:1511.08630.
Freitas, L., Silva, T., and Mendes, M. (2016). Evaluation of spotify: an evaluation textual experience using the maltu methodology. pages 1–4.
Hedegaard, S. and Simonsen, J. G. (2013). Extracting usability and user experience information from online user reviews. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13, pages 2089–2098, New York, NY, USA. ACM.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Lima, A. M., Silva, P. B., Cruz, L. A., and Mendes, M. S. (2017). Investigating the polarity of user postings in a social system. In International Conference on Social Computing and Social Media, pages 246–257. Springer.
Loper, E. and Bird, S. (2002). Nltk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1, ETMTNLP ’02, pages 63–70, Stroudsburg, PA, USA. Association for Computational Linguistics.
McKinney, W. et al. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, volume 445, pages 51–56. Austin, TX.
Mendes, M. S. (2015). MALTU – um modelo para avaliação da interação em sistemas sociais a partir da linguagem textual do usuário. PhD thesis, Federal University of Ceará.
Mendes, M. S. and Furtado, E. S. (2017). Uux-posts: a tool for extracting and classifying postings related to the use of a system. In Proceedings of the 8th Latin American Conference on Human-Computer Interaction, page 2. ACM.
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. J. Mach. Learn. Res., 12:2825–2830.
Pereira, R., Baranauskas, M. C. C., and da Silva, S. R. P. (2010). Softwares sociais: uma visão orientada a valores. In Proceedings of the IX Symposium on Human Factors in Computing Systems, pages 149–158. Brazilian Computer Society.
Preece, J., Rogers, Y., and Sharp, H. (2015). Interaction design: beyond human-computer interaction. John Wiley & Sons.
Schuster, M. and Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681.
Shaw, D. (1996). Handbook of usability testing: How to plan, design, and conduct effective tests. Journal of the American Society for Information Science, 47(3):258–259. van der Walt, S., Colbert, S. C., and Varoquaux, G. (2011). The numpy array: a structure for efficient numerical computation. CoRR, abs/1102.1523.
van der Walt, S., Colbert, S. C., and Varoquaux, G. (2011). The numpy array: a structure for efficient numerical computation. CoRR, abs/1102.1523.
Publicado
15/10/2019
Como Citar
OLIVEIRA, Hismael de; ARRUDA, Alexandre; MENDES, Marilia.
Identificação Automática de Postagens Relacionadas ao Uso Através de Modelos de Aprendizagem Profunda. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 587-598.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9317.
