Evaluating an Aspect Extraction Method for Opinion Mining in the Portuguese Language
Resumo
The opinion issued by consumers of products and services has become increasingly valued, both by other consumers and by companies. The automatic interpretation of review texts to generate information is of paramount importance. With opinion mining at the aspect level, it is possible to extract and summarize opinions about different components of a product or service. This paper evaluates the behavior of a method for extracting aspects using natural language processing tools for the Portuguese language. The aim is to investigate the maturity of the tools for Portuguese compared to the already consolidated tools for the English language. The evaluation was carried out in three datasets from two different domains with original texts in Portuguese and their translations into English, and vice versa, and the results indicate that there is no difference between languages.
Palavras-chave:
aspect extraction, opinion mining, Portuguese language, sentiment analysis
Referências
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Hu, M. and Liu, B. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA, pp. 168–177, 2004.
Jakob, N. and Gurevych, I. Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. USA, pp. 1035–1045, 2010.
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Pereira, D. A. A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, July, 2020.
Poria, S., Cambria, E., Howard, N., Huang, G.-B., and Hussain, A. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing vol. 174, pp. 50–59, 2016.
Saias, J., ao, M. M., and Oliveira, E. Detailing sentiment analysis to consider entity aspects: An approach for portuguese short texts. Transactions on Machine Learning and Artificial Intelligence 6 (2): 26–35, 2018.
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Zhang, L., Wang, S., and Liu, B. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (4): e1253, 2018.
Zhuang, L., Jing, F., and Zhu, X.-Y. Movie review mining and summarization. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York, NY, USA, pp. 43–50, 2006.
Baccianella, S., Esuli, A., and Sebastiani, F. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). Valletta, Malta, pp. 2200–2204, 2010.
Cambria, E., Poria, S., Hazarika, D., and Kwok, K. Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceedings of the The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI). AAAI Press, New Orleans, USA, pp. 1795–1802, 2018.
Catharin, L. G. and Feltrim, V. D. Finding opinion targets in news comments and book reviews. In Proceedings of the 13th International Conference on Computational Processing of the Portuguese Language (PROPOR). Springer International Publishing, Canela, Brazil, pp. 375–384, 2018.
Chaturvedi, I., Cambria, E., Welsch, R. E., and Herrera, F. Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Information Fusion vol. 44, pp. 65–77, 2018.
Farias, D. S., Matsuno, I. P., Marcacini, R. M., and Rezende, S. O. Opinion-meter: A framework for aspect-based sentiment analysis. In Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web (Webmedia). ACM, Teresina, Brazil, pp. 351–354, 2016.
Freitas, C., Motta, E., Milidiú, R., and César, J. Sparkling Vampire... lol! Annotating Opinions in a Book Review Corpus. In Sandra Aluísio and Stella E. O. Tagnin (eds.), New Language Technologies and Linguistic Research: A Two-Way Road. Cambridge Scholars Publishing, 2014.
Freitas, L. A. D. and Vieira, R. Ontology-based feature-level sentiment analysis in portuguese reviews. International Journal of Business Information Systems 32 (1): 30–55, 2019.
Hu, M. and Liu, B. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA, pp. 168–177, 2004.
Jakob, N. and Gurevych, I. Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. USA, pp. 1035–1045, 2010.
Lafferty, J., McCallum, A., and Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, USA, pp. 282–289, 2001.
Liu, B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5 (1): 1–167, 2012.
Lo, S. L., Cambria, E., Chiong, R., and Cornforth, D. Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artificial Intelligence Review 48 (4): 499–527, Dec, 2017.
Machado, M. T., Pardo, T. A. S., and Ruiz, E. E. S. Analysis of unsupervised aspect term identification methods for portuguese reviews. In Anais do XIV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC).
SBC, Uberlândia, Brazil, pp. 239–249, 2017.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., and McClosky, D. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Baltimore, Maryland, pp. 55–60, 2014.
Pereira, D. A. A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, July, 2020.
Poria, S., Cambria, E., Howard, N., Huang, G.-B., and Hussain, A. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing vol. 174, pp. 50–59, 2016.
Saias, J., ao, M. M., and Oliveira, E. Detailing sentiment analysis to consider entity aspects: An approach for portuguese short texts. Transactions on Machine Learning and Artificial Intelligence 6 (2): 26–35, 2018.
Souza, M., Vieira, R., Busetti, D., Chishman, R., and Alves, I. M. Construction of a Portuguese opinion lexicon from multiple resources. In Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology. Mato Grosso, Brazil, 2011.
Yadollahi, A., Shahraki, A. G., and Zaiane, O. R. Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys 50 (2): 25:1–25:33, May, 2017.
Zhang, L. and Liu, B. Aspect and entity extraction for opinion mining. Springer, 2014. Book Chapter, Volume 1 of the series Studies in Big Data.
Zhang, L., Wang, S., and Liu, B. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (4): e1253, 2018.
Zhuang, L., Jing, F., and Zhu, X.-Y. Movie review mining and summarization. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York, NY, USA, pp. 43–50, 2006.
Publicado
20/10/2020
Como Citar
CARDOSO, Breno; PEREIRA, Denilson.
Evaluating an Aspect Extraction Method for Opinion Mining in the Portuguese Language. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 137-144.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2020.11969.
