Extrair Conhecimento em Comentários Gerados em Mídias Sociais Utilizando Análise de Sentimentos
Abstract
The internet provide social media tools by which people communicate and influence the social, political and economic behavior of others. In this context, this work shows how the process of Feeling Analysis can obtain the evaluation of people in relation to products through the analysis of texts. The contributions of this article were the creation of the database and generation of the predictive model.
References
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Aranha, C. N. (2007). Processamento Automático para Mineração de Textos em Portugu ês: Sob o Enfoque da Inteligência Computacional. PhD thesis, PUC-Rio.
Bird, S., Klein, E., and Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. ”O’Reilly Media, Inc.”.
Carvalho Filho, J. A. (2014). Mineração de textos: Análise de sentimento utilizando tweets referentes à copa do mundo 2014.
D’Adrea, A., Ferri, F., Grifoni, P., and Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. In International Journal of Computer Applications, page 0975 – 8887. IJCA.
Hailong, Z., Wenyan, G., and Bo, J. (2014). [ieee 2014 11th web information system and application conference (wisa) - tianjin, china (2014.9.12-2014.9.14)] 2014 11th web information system and application conference - machine learning and lexicon based methods for sentiment classification: A survey.
Kaur, A. and Gupta, V. (2013). A survey on sentiment analysis and opinion mining techniques. In Journal of Emerging Technologies in Web Intelligence, pages 367–371. JETWI.
Kaur, H., Mangat, V., et al. (2017). A survey of sentiment analysis techniques. In I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2017 International Conference on, pages 921–925. IEEE.
Liddy, E. (2001). Natural language processing. In In Encyclopedia of Library and Information Science, New York: Marcel Decker.
Nitin, I. and Damerau, F. J. (2010). Handbook of natural language processing. ISBN 9781420085921.
Vasinek, M., Plato, J., and Snasel, V. (2016). Limitations on low variance k-fold cross validation in learning set of rules inducers. In Intelligent Networking and Collaborative Systems (INCoS), 2016 International Conference on, pages 207–214. IEEE.
Vinodhini, G. and Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining. In International Journal 2.6.
Published
2018-10-16
How to Cite
OLIVEIRA, Regenildo; JUNIOR, Halysson; VELOSO, Artur; RODRIGUES, Antônio; SILVA, Marcello; OLIVEIRA, Davi; RABELO, Ricardo; SOBRAL, Jose.
Extrair Conhecimento em Comentários Gerados em Mídias Sociais Utilizando Análise de Sentimentos. In: REGIONAL SCHOOL ON INFORMATICS OF PIAUÍ (ERI-PI), 4. , 2018, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
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p. 26 - 31.
