Investigating sets of linguistic features for two sentiment analysis tasks in Brazilian Portuguese web reviews
Resumo
Identifying subjective sentences and classifying the polarity of subjective sentences are two important tasks in sentiment analysis. Besides being a hot topic, there is still a lack of resources to perform the mentioned sentiment analysis tasks in the Portuguese language, with its syntactic specificities. This paper describes the identified challenges and next steps in an initial study regarding the classification of subjectivity and polarity of sentences with a small set of syntactic features extracted directly from the text. Our approach reached satisfying results in experiments with two classic machine learning models in four datasets consisting of user reviews from different domains.
Referências
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Bum Chul Kwon, Sung-Hee Kim, Timothy Duket, Adrián Catalán, and Ji Soo Yi.2015. Do people really experience information overload while reading online reviews? International Journal of Human-Computer Interaction 31, 12 (2015), 959–973.
Bing Liu. 2015. Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press.
Silvia MW Moraes, André LL Santos, Matheus Redecker, Rackel M Machado, and Felipe R Meneguzzi. 2016. Comparing approaches to subjectivity classification: A study on portuguese tweets. In International Conference on Computational Processing of the Portuguese Language. Springer, 86–94.
Miguel Oliveira and Tiago Melo. 2020. Investigating Sentences Features for Subjectivity and Polarity Classification in Brazilian Portuguese. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional. SBC, Porto Alegre, RS, Brasil, 270–281. https://doi.org/10.5753/eniac.2020.12135
Denilson Alves Pereira. 2020. A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review (2020), 1–29.
J Ross Quinlan. 1996. Boosting first-order learning. In International Workshop on Algorithmic Learning Theory. Springer, 143–155.
Kumar Ravi and Vadlamani Ravi. 2015. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems 89 (2015),14–46.
Kim Schouten and Flavius Frasincar. 2015. Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering 28, 3 (2015),813–830.
Milton Stiilpen Júnior. 2016. Um arcabouço de processamento de textos informais em português brasileiro para aplicações de mineração de dados. Master’s thesis. Universidade Federal de Ouro Preto.
Suge Wang, Deyu Li, Xiaolei Song, Yingjie Wei, and Hongxia Li. 2011. A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Systems with Applications 38, 7 (2011), 8696–8702.
Ashima Yadav and Dinesh Kumar Vishwakarma. 2020. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review 53, 6 (2020), 4335–4385.
Lin Yue, Weitong Chen, Xue Li, Wanli Zuo, and Minghao Yin. 2019. A survey of sentiment analysis in social media. Knowledge and Information Systems (2019),1–47.
Lei Zhang, Shuai Wang, and Bing Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery8, 4 (2018), e1253.
Matheus Araújo, Pollyanna Gonçalves, Meeyoung Cha, and Fabrício Benevenuto.2014. iFeel: a system that compares and combines sentiment analysis methods. In Proceedings of the 23rd International Conference on World Wide Web. 75–78.
Pedro Paulo Balage Filho. 2017. Aspect extraction in sentiment analysis for portuguese language. Ph.D. Dissertation. Universidade de Sao Paulo.
Larissa Britto and Luciano Pacífi co. 2019. Análise de Sentimentos para Revisões de Aplicativos Mobile em Português Brasileiro. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional. SBC, 1080–1090.
Erik Cambria, Björn Schuller, Yunqing Xia, and Catherine Havasi. 2013. Newavenues in opinion mining and sentiment analysis. IEEE Intelligent systems 28, 2(2013), 15–21.
Jose M Chenlo and David E Losada. 2014. An empirical study of sentence features for subjectivity and polarity classification. Information Sciences 280 (2014), 275–288.
Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273–297.
Tiago de Melo, Altigran S da Silva, Edleno S de Moura, and Pável Calado. 2019. OpinionLink: Leveraging user opinions for product catalog enrichment. Information Processing & Management 56, 3 (2019), 823–843.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
Fernando Leandro dos Santos and Marcelo Ladeira. 2014. The role of text pre-processing in opinion mining on a social media language dataset. In 2014 Brazilian Conference on Intelligent Systems. IEEE, 50–54.
Cláudia Freitas, Eduardo Motta, Ruy L Milidiú, and Juliana César. 2014. Sparkling vampire... lol! annotating opinions in a book review corpus. New Language Technologies and Linguistic Research: A Two-Way Road (2014), 128–146.
Bum Chul Kwon, Sung-Hee Kim, Timothy Duket, Adrián Catalán, and Ji Soo Yi.2015. Do people really experience information overload while reading online reviews? International Journal of Human-Computer Interaction 31, 12 (2015), 959–973.
Bing Liu. 2015. Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press.
Silvia MW Moraes, André LL Santos, Matheus Redecker, Rackel M Machado, and Felipe R Meneguzzi. 2016. Comparing approaches to subjectivity classification: A study on portuguese tweets. In International Conference on Computational Processing of the Portuguese Language. Springer, 86–94.
Miguel Oliveira and Tiago Melo. 2020. Investigating Sentences Features for Subjectivity and Polarity Classification in Brazilian Portuguese. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional. SBC, Porto Alegre, RS, Brasil, 270–281. https://doi.org/10.5753/eniac.2020.12135
Denilson Alves Pereira. 2020. A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review (2020), 1–29.
J Ross Quinlan. 1996. Boosting first-order learning. In International Workshop on Algorithmic Learning Theory. Springer, 143–155.
Kumar Ravi and Vadlamani Ravi. 2015. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems 89 (2015),14–46.
Kim Schouten and Flavius Frasincar. 2015. Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering 28, 3 (2015),813–830.
Milton Stiilpen Júnior. 2016. Um arcabouço de processamento de textos informais em português brasileiro para aplicações de mineração de dados. Master’s thesis. Universidade Federal de Ouro Preto.
Suge Wang, Deyu Li, Xiaolei Song, Yingjie Wei, and Hongxia Li. 2011. A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Systems with Applications 38, 7 (2011), 8696–8702.
Ashima Yadav and Dinesh Kumar Vishwakarma. 2020. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review 53, 6 (2020), 4335–4385.
Lin Yue, Weitong Chen, Xue Li, Wanli Zuo, and Minghao Yin. 2019. A survey of sentiment analysis in social media. Knowledge and Information Systems (2019),1–47.
Lei Zhang, Shuai Wang, and Bing Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery8, 4 (2018), e1253.
Publicado
30/11/2020
Como Citar
OLIVEIRA, Miguel V.; MELO, Tiago de.
Investigating sets of linguistic features for two sentiment analysis tasks in Brazilian Portuguese web reviews. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 26. , 2020, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 45-48.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2020.13060.
