Métodos baseados em léxico para extração de aspectos de opiniões em português
Resumo
Na área de análise de sentimentos, a tarefa de extração de aspectos consiste usualmente em identificar os aspectos de opinião em revisões produzidas por usuários sobre produtos que adquiriram. É uma tarefa de base para diversas outras, desde classificação de polaridade à produção de sumários de opinião. Neste artigo, estudamos alguns métodos para extração de aspectos em opiniões escritas em português. Em especial, focamos em métodos baseados em léxico, buscando avaliar suas potencialidades e limitações.
Palavras-chave:
Mineração de opinião baseada em aspectos, ontologia, word embedding
Referências
Balage Filho, P.P. (2017). Aspect Extraction in Sentiment Analysis for Portuguese. Tese de Doutorado. Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. São Carlos-SP, October, 77p.
Bertaglia, T.F.C. and Nunes, M.G.V. (2016). Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization. In the Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pp. 112-120.
Catharin, L.G. and Feltrim, V.D. (2018). Finding Opinion Targets in News Comments and Book Reviews. In the Proceedings of the 13th International Conference on Computational Processing of the Portuguese Language (PROPOR) (LNAI 11122), pp. 375-384.
Chandrasekaran, B.; Josephson, J.; Benjamins, V. R. (1999). What Are Ontologies, and Why Do We Need Them? IEEE Intelligent Systems and their Applications, Vol. 14, N. 1, pp. 20-26.
Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 4171-4186.
Gaillat, T.; Stearns, B.; Sridhar, G; McDermott, R.; Zarrouk, M. (2018). Implicit and Explicit Aspect Extraction in Financial Microblogs. In the Proceedings of the 1st Workshop on Economics and Natural Language Processing, pp. 55–61.
Grosz, B.J.; Joshi, A.K.; Weinstein, S. (1995). Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics, Vol. 21, N. 2, pp. 204-225.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In the Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 168-177.
Hartmann, N.S.; Avanço, L.V.; Balage Filho, P.P.; Duran, M.S.; Nunes, M.G.V.; Pardo, T.A.S.; Aluísio, S.M. (2014). A Large Corpus of Product Reviews in Portuguese: Tackling Out-Of-Vocabulary Words. In the Proceedings of the 9th Language Resources and Evaluation Conference (LREC), pp. 3865-3871.
Jurafsky, D. and Martin, J.H. (2008). Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
Liu, B; Hu, M.; Cheng, J. (2005). Opinion Observer: Analyzing and Comparing Opinions on the Web. In the Proceedings of the 14th International Conference on World Wide Web, pp. 342-351.
Liu, P.; Joty, S.; Meng, H. (2015). Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. In the Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1433–1443.
López Condori, R.E. and Pardo, T.A.S. (2017). Opinion Summarization Methods: Comparing and Extending Extractive and Abstractive Approaches. Expert Systems with Applications (ESWA), Vol. 78, pp. 124-134.
Mikolov, T.; Corrado, G.; Chen, K.; Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ArXiv: 1301.3781.
Oliveira, F.W.C. and Feltrim, V.D. (2015). Extração de Alvos em Comentários de Notícias em Português baseada na Teoria da Centralização. In the Proceedings of Symposium in Information and Human Language Technology (STIL), pp. 63-67.
Silva, R.R. e Pardo, T.A.S. (2019). Córpus 4P: um córpus anotado de opiniões em português sobre produtos eletrônicos para fins de sumarização contrastiva de opinião. In Anais da 6a Jornada de Descrição do Português (JDP), pp. 330-338.
Souza, F.; Nogueira, R.; Lotufo, R. (2020). Portuguese Named Entity Recognition using BERT-CRF. arXiv:1909.10649v2
Toh, Z. and Su, J. (2015). NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction. In the Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval), pp. 496–501.
Vargas, F.A. and Pardo, T,A.S. (2018a). Hierarchical clustering of aspects for opinion mining: a corpus study. In M.J.B. Finatto, R.R. Rebechi, S. Sarmento and A.E.P. Bocorny (eds.), Linguística de Corpus: Perspectivas, pp. 69-91. Porto Alegre: Instituto de Letras da UFRGS. 580p.
Vargas, F.A. and Pardo, T.A.S. (2018b). Aspect clustering methods for sentiment analysis. In the Proceedings of the 13th International Conference on the Computational Processing of Portuguese (PROPOR) (LNAI 11122), pp. 365-374.
Vargas, F.A. and Pardo, T.A.S. (2020). Linguistic Rules for Fine-Grained Opinion Extraction. In the Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, pp. 1-6.
Bertaglia, T.F.C. and Nunes, M.G.V. (2016). Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization. In the Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pp. 112-120.
Catharin, L.G. and Feltrim, V.D. (2018). Finding Opinion Targets in News Comments and Book Reviews. In the Proceedings of the 13th International Conference on Computational Processing of the Portuguese Language (PROPOR) (LNAI 11122), pp. 375-384.
Chandrasekaran, B.; Josephson, J.; Benjamins, V. R. (1999). What Are Ontologies, and Why Do We Need Them? IEEE Intelligent Systems and their Applications, Vol. 14, N. 1, pp. 20-26.
Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 4171-4186.
Gaillat, T.; Stearns, B.; Sridhar, G; McDermott, R.; Zarrouk, M. (2018). Implicit and Explicit Aspect Extraction in Financial Microblogs. In the Proceedings of the 1st Workshop on Economics and Natural Language Processing, pp. 55–61.
Grosz, B.J.; Joshi, A.K.; Weinstein, S. (1995). Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics, Vol. 21, N. 2, pp. 204-225.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In the Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 168-177.
Hartmann, N.S.; Avanço, L.V.; Balage Filho, P.P.; Duran, M.S.; Nunes, M.G.V.; Pardo, T.A.S.; Aluísio, S.M. (2014). A Large Corpus of Product Reviews in Portuguese: Tackling Out-Of-Vocabulary Words. In the Proceedings of the 9th Language Resources and Evaluation Conference (LREC), pp. 3865-3871.
Jurafsky, D. and Martin, J.H. (2008). Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
Liu, B; Hu, M.; Cheng, J. (2005). Opinion Observer: Analyzing and Comparing Opinions on the Web. In the Proceedings of the 14th International Conference on World Wide Web, pp. 342-351.
Liu, P.; Joty, S.; Meng, H. (2015). Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. In the Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1433–1443.
López Condori, R.E. and Pardo, T.A.S. (2017). Opinion Summarization Methods: Comparing and Extending Extractive and Abstractive Approaches. Expert Systems with Applications (ESWA), Vol. 78, pp. 124-134.
Mikolov, T.; Corrado, G.; Chen, K.; Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ArXiv: 1301.3781.
Oliveira, F.W.C. and Feltrim, V.D. (2015). Extração de Alvos em Comentários de Notícias em Português baseada na Teoria da Centralização. In the Proceedings of Symposium in Information and Human Language Technology (STIL), pp. 63-67.
Silva, R.R. e Pardo, T.A.S. (2019). Córpus 4P: um córpus anotado de opiniões em português sobre produtos eletrônicos para fins de sumarização contrastiva de opinião. In Anais da 6a Jornada de Descrição do Português (JDP), pp. 330-338.
Souza, F.; Nogueira, R.; Lotufo, R. (2020). Portuguese Named Entity Recognition using BERT-CRF. arXiv:1909.10649v2
Toh, Z. and Su, J. (2015). NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction. In the Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval), pp. 496–501.
Vargas, F.A. and Pardo, T,A.S. (2018a). Hierarchical clustering of aspects for opinion mining: a corpus study. In M.J.B. Finatto, R.R. Rebechi, S. Sarmento and A.E.P. Bocorny (eds.), Linguística de Corpus: Perspectivas, pp. 69-91. Porto Alegre: Instituto de Letras da UFRGS. 580p.
Vargas, F.A. and Pardo, T.A.S. (2018b). Aspect clustering methods for sentiment analysis. In the Proceedings of the 13th International Conference on the Computational Processing of Portuguese (PROPOR) (LNAI 11122), pp. 365-374.
Vargas, F.A. and Pardo, T.A.S. (2020). Linguistic Rules for Fine-Grained Opinion Extraction. In the Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, pp. 1-6.
Publicado
30/06/2020
Como Citar
COSTA, Raul Wagner Martins; PARDO, Thiago Alexandre Salgueiro.
Métodos baseados em léxico para extração de aspectos de opiniões em português. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 9. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 61-72.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2020.11163.
