Extrair Conhecimento em Comentários Gerados em Mídias Sociais Utilizando Análise de Sentimentos
Resumo
A internet proporcionar ferramentas de mídias sociais pelas quais as pessoas se comunicam e influenciam o comportamento social, político e economico de outras pessoas. Neste contexto, este trabalho mostra como o processo de Análise de Sentimentos pode obter a avaliação das pessoas em relação a produtos atraves da análise de textos. As contribuições deste artigo foram a criação da base de dados e geração do modelo preditivo.
Referências
Ali, N. M., Jun, S. W., Karis, M. S., Ghazaly, M. M., and Aras, M. S. M. (2016). Object classification and recognition using bag-of-words (bow) model. In Signal Processing & Its Applications (CSPA), 2016 IEEE 12th International Colloquium on, pages 216– 220. IEEE.
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.
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.
Publicado
16/10/2018
Como Citar
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: ESCOLA REGIONAL DE INFORMÁTICA DO PIAUÍ (ERI-PI), 4. , 2018, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 26 - 31.
