Análise de Sentimentos para Revisões de Aplicativos Mobile em Português Brasileiro
Resumo
Mesmo com a crescente popularidade da Análise de Sentimentos (AS), a quantidade de recursos e ferramentas disponíveis para o português brasileiro ainda é limitada. Neste trabalho serão descritas as etapas para o desenvolvimento de uma base de dados em português no domínio de aplicativos móveis, para aplicações em AS. Além disso, a base proposta será utilizada para a comparação dos principais métodos utilizados na literatura de AS, como as Redes Neurais Recorrentes.
Palavras-chave:
Análise de Sentimentos, Análise de Revisões dos Usuários, Processamento de Linguagem Natural, Mineração de Texto
Referências
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Bergsma, S., Jung, D., Lau, R., Wang, Y., and Wang, S. (2005). Machine learning approaches to sentiment classification cmput 551 : Course project winter , 2005.
Bhatnagar, R. (2018). Machine Learning and Big Data Processing: A Technological Perspective and Review, pages 468–478.
Blitzer, J., McDonald, R., and Pereira, F. (2006). Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, pages 120–128, Stroudsburg, PA, USA. Association for Computational Linguistics.
Brum, H. B. and das Graças Volpe Nunes, M. (2017). Building a sentiment corpus of tweets in brazilian portuguese. CoRR, abs/1712.08917.
Criminisi, A., Konukoglu, E., and Shotton, J. (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning.
de Aguiar, E. J., Faiçal, B. S., Ueyama, J., Silva, G. C., and Menolli, A. (2018). Análise de sentimento em redes sociais para a lı́ngua portuguesa utilizando algoritmos de classificação. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, Porto Alegre, RS, Brasil. SBC.
de Souza, J. G. R., de Paiva Oliveira, A., and Moreira, A. (2018). Development of a brazilian portuguese hotel’s reviews corpus. In PROPOR.
Fang, X. and Zhan, J. (2015). Sentiment analysis using product review data. J Big Data, 2.
Farias, D. H. and Rosso, P. (2017). Chapter 7 - irony, sarcasm, and sentiment analysis. In Pozzi, F. A., Fersini, E., Messina, E., and Liu, B., editors, Sentiment Analysis in Social Networks, pages 113 – 128. Morgan Kaufmann, Boston.
Guan, X., Li, Y., Gong, H., Sun, H., and Zhou, C. (2018). An improved svm for book review sentiment polarity analysis. In 2018 International Conference on Transportation Logistics, Information Communication, Smart City (TLICSC 2018). Atlantis Press.
Hegde, Y. and Padma, S. K. (2017). Sentiment analysis using random forest ensemble for mobile product reviews in kannada. In 2017 IEEE 7th International Advance Computing Conference (IACC), pages 777–782.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9:1735–80.
John, A., John, A., and Sheik, R. (2019). Context deployed sentiment analysis using hybrid lexicon. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pages 1–5.
Jose, R. and Chooralil, V. S. (2016). Prediction of election result by enhanced sentiment analysis on twitter data using classifier ensemble approach. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), pages 64–67.
Lu, K. and Wu, J. (2019). Sentiment analysis of film review texts based on sentiment dictionary and svm. In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, ICIAI 2019, pages 73–77, New York, NY, USA. ACM.
Martinazzo, B. and Paraiso, E. C. (2010). Identificação de emoções em notı́cias curtas.
Moraes, S. M. W., Manssour, I. H., and Silveira, M. S. (2015). 7x1-PT: um corpus extraı́do do twitter para análise de sentimentos em lı́ngua portuguesa (7x1-PT: a corpus extracted from twitter for sentiment analysis in Portuguese language). In Proceedings of the 10th Brazilian Symposium in Information and Human Language Technology, pages 21–25, Natal, Brazil. Sociedade Brasileira de Computação.
Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., and Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. In Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pages 751–760, New York, NY, USA. ACM.
Ramadhan, W. P., Novianty, S. T. M. T. A., and Setianingsih, S. T. M. T. C. (2017). Sentiment analysis using multinomial logistic regression. In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pages 46–49.
Ramanathan, V. and Meyyappan, T. (2019). Twitter text mining for sentiment analysis on people’s feedback about oman tourism. In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), pages 1–5.
Rane, A. and Kumar, A. (2018). Sentiment classification system of twitter data for us airline service analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), volume 01, pages 769–773.
Rathi, M., Malik, A., Varshney, D., Sharma, R., and Mendiratta, S. (2018). Sentiment analysis of tweets using machine learning approach. In 2018 Eleventh International Conference on Contemporary Computing (IC3), pages 1–3.
Schäfer, R. and Bildhauer, F. (2015). Web corpus construction roland schäfer and felix bildhauer (freie universität berlin) morgan claypool (synthesis lectures on human laguage technologies, edited by graeme hirst, volume 22), 2013, 145 pages, paper-bound, isbn 9781608459834, doi:10.2200/s00508ed1v01y201305hlt022. Computational Linguistics, 41:161–163.
Sharfuddin, A. A., Tihami, M. N., and Islam, M. S. (2018). A deep recurrent neural network with bilstm model for sentiment classification. 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pages 1–4.
Souza, M. and Vieira, R. (2012). Sentiment analysis on twitter data for portuguese language. In Caseli, H., Villavicencio, A., Teixeira, A., and Perdigão, F., editors, Comptational Processing of the Portuguese Language, pages 241–247, Berlin, Heidelberg. Springer Berlin Heidelberg.
Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, pages 417–424, Stroudsburg, PA, USA. Association for Computational Linguistics.
Vapnik, V. (1991). Principles of risk minimization for learning theory. In Proceedings of the 4th International Conference on Neural Information Processing Systems, NIPS’91, pages 831–838, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Vargiu, E. and Urru, M. (2012). Exploiting web scraping in a collaborative filtering- based approach to web advertising. Artif. Intell. Research, 2:44–54.
Wang, L., Niu, J., and Yu, S. (2019). Sentidiff: Combining textual information and sentiment diffusion patterns for twitter sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, pages 1–1.
Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., and Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. In PAKDD Workshops.
Zuo, Z. (2018). Sentiment analysis of steam review datasets using naive bayes and decision tree classifier.
Bergsma, S., Jung, D., Lau, R., Wang, Y., and Wang, S. (2005). Machine learning approaches to sentiment classification cmput 551 : Course project winter , 2005.
Bhatnagar, R. (2018). Machine Learning and Big Data Processing: A Technological Perspective and Review, pages 468–478.
Blitzer, J., McDonald, R., and Pereira, F. (2006). Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, pages 120–128, Stroudsburg, PA, USA. Association for Computational Linguistics.
Brum, H. B. and das Graças Volpe Nunes, M. (2017). Building a sentiment corpus of tweets in brazilian portuguese. CoRR, abs/1712.08917.
Criminisi, A., Konukoglu, E., and Shotton, J. (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning.
de Aguiar, E. J., Faiçal, B. S., Ueyama, J., Silva, G. C., and Menolli, A. (2018). Análise de sentimento em redes sociais para a lı́ngua portuguesa utilizando algoritmos de classificação. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, Porto Alegre, RS, Brasil. SBC.
de Souza, J. G. R., de Paiva Oliveira, A., and Moreira, A. (2018). Development of a brazilian portuguese hotel’s reviews corpus. In PROPOR.
Fang, X. and Zhan, J. (2015). Sentiment analysis using product review data. J Big Data, 2.
Farias, D. H. and Rosso, P. (2017). Chapter 7 - irony, sarcasm, and sentiment analysis. In Pozzi, F. A., Fersini, E., Messina, E., and Liu, B., editors, Sentiment Analysis in Social Networks, pages 113 – 128. Morgan Kaufmann, Boston.
Guan, X., Li, Y., Gong, H., Sun, H., and Zhou, C. (2018). An improved svm for book review sentiment polarity analysis. In 2018 International Conference on Transportation Logistics, Information Communication, Smart City (TLICSC 2018). Atlantis Press.
Hegde, Y. and Padma, S. K. (2017). Sentiment analysis using random forest ensemble for mobile product reviews in kannada. In 2017 IEEE 7th International Advance Computing Conference (IACC), pages 777–782.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9:1735–80.
John, A., John, A., and Sheik, R. (2019). Context deployed sentiment analysis using hybrid lexicon. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pages 1–5.
Jose, R. and Chooralil, V. S. (2016). Prediction of election result by enhanced sentiment analysis on twitter data using classifier ensemble approach. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), pages 64–67.
Lu, K. and Wu, J. (2019). Sentiment analysis of film review texts based on sentiment dictionary and svm. In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, ICIAI 2019, pages 73–77, New York, NY, USA. ACM.
Martinazzo, B. and Paraiso, E. C. (2010). Identificação de emoções em notı́cias curtas.
Moraes, S. M. W., Manssour, I. H., and Silveira, M. S. (2015). 7x1-PT: um corpus extraı́do do twitter para análise de sentimentos em lı́ngua portuguesa (7x1-PT: a corpus extracted from twitter for sentiment analysis in Portuguese language). In Proceedings of the 10th Brazilian Symposium in Information and Human Language Technology, pages 21–25, Natal, Brazil. Sociedade Brasileira de Computação.
Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., and Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. In Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pages 751–760, New York, NY, USA. ACM.
Ramadhan, W. P., Novianty, S. T. M. T. A., and Setianingsih, S. T. M. T. C. (2017). Sentiment analysis using multinomial logistic regression. In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pages 46–49.
Ramanathan, V. and Meyyappan, T. (2019). Twitter text mining for sentiment analysis on people’s feedback about oman tourism. In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), pages 1–5.
Rane, A. and Kumar, A. (2018). Sentiment classification system of twitter data for us airline service analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), volume 01, pages 769–773.
Rathi, M., Malik, A., Varshney, D., Sharma, R., and Mendiratta, S. (2018). Sentiment analysis of tweets using machine learning approach. In 2018 Eleventh International Conference on Contemporary Computing (IC3), pages 1–3.
Schäfer, R. and Bildhauer, F. (2015). Web corpus construction roland schäfer and felix bildhauer (freie universität berlin) morgan claypool (synthesis lectures on human laguage technologies, edited by graeme hirst, volume 22), 2013, 145 pages, paper-bound, isbn 9781608459834, doi:10.2200/s00508ed1v01y201305hlt022. Computational Linguistics, 41:161–163.
Sharfuddin, A. A., Tihami, M. N., and Islam, M. S. (2018). A deep recurrent neural network with bilstm model for sentiment classification. 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pages 1–4.
Souza, M. and Vieira, R. (2012). Sentiment analysis on twitter data for portuguese language. In Caseli, H., Villavicencio, A., Teixeira, A., and Perdigão, F., editors, Comptational Processing of the Portuguese Language, pages 241–247, Berlin, Heidelberg. Springer Berlin Heidelberg.
Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, pages 417–424, Stroudsburg, PA, USA. Association for Computational Linguistics.
Vapnik, V. (1991). Principles of risk minimization for learning theory. In Proceedings of the 4th International Conference on Neural Information Processing Systems, NIPS’91, pages 831–838, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Vargiu, E. and Urru, M. (2012). Exploiting web scraping in a collaborative filtering- based approach to web advertising. Artif. Intell. Research, 2:44–54.
Wang, L., Niu, J., and Yu, S. (2019). Sentidiff: Combining textual information and sentiment diffusion patterns for twitter sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, pages 1–1.
Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., and Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. In PAKDD Workshops.
Zuo, Z. (2018). Sentiment analysis of steam review datasets using naive bayes and decision tree classifier.
Publicado
15/10/2019
Como Citar
BRITTO, Larissa; PACÍFICO, Luciano.
Análise de Sentimentos para Revisões de Aplicativos Mobile em Português Brasileiro. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1080-1090.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9359.
