Automatic Identification of Postings Related to the Use Through Deep Learning Models
Abstract
Some posts in Social Systems (SS) can bring important informations about the use of that SS, this we call Postings Related to the Use (PRU). The PRU are important tolls to systems evaluate, in this case textual evaluation. There are softwares ables to identify PRU, however they have small precision when compared to manual classifications. Given this problem, the present work seeks to overcome this deficiency by using deep learning models for the automatic identification of PRU in SS, in this case the social network \textit{Twitter}. In performed tests, our model has higher precision when compared to anothers used for this work.
Keywords:
Postings Related to the Use, Deep Learning, Social Systems
References
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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.
Published
2019-10-15
How to Cite
OLIVEIRA, Hismael de; ARRUDA, Alexandre; MENDES, Marilia.
Automatic Identification of Postings Related to the Use Through Deep Learning Models. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (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.
