Análise de Métodos de Extração de Aspectos em Opiniões Regulares
DOI:
https://doi.org/10.5753/isys.2020.796Keywords:
Mineração de Opinião, Análise de Sentimentos, Extração de AspectosAbstract
Este trabalho apresenta uma análise comparativa entre as principais abordagens usadas na tarefa de Extração de Aspectos em comentários sobre produtos e serviços em web sites. Adaptações de quatro métodos de extração de aspectos foram implementadas e avaliadas usando dois Corpora distintos: um em português e outro em inglês. Nos experimentos realizados foi observado que a abordagem usando aprendizagem supervisionada (redes neurais convolucionais) obteve melhores resultados que as demais.
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Referências
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Archak, N., Ghose, A. and Ipeirotis, P. G. (2007) “Show me the money!: deriving the pricing power of product features by mining consumer reviews”. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 56-65.
Asnani, K., and Pawar, J. D. (2018). Extraction of Code-mixed Aspect Topics in Semantic Representation. Computación y Sistemas, 22(1), 55-63.
Bickart, B. and Schindler, R. (2001) “Internet forums as influential sources of consumer information”. Journal of Interactive Marketing, 15(3), 31-40.
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003) “Latent dirichlet allocation”. Journal of Machine Learning Research, 3, 993-1022.
Bonchi, F., Castillo, C., Gionis, A. and Jaimes, A. (2011) “Social network analysis and mining for business applications”. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 22:1-22:37.
Branavan, S. R. K., Chen, H., Eisenstein, J. and Barzilay, R. (2009) “Learning document-level semantic properties from free-text annotations”. In: Journal of Artificial Intelligence Research, 34, 569-603.
Chen, Y. and Xie, J. (2008) “Online consumer review: Word-of-mouth as a new element of marketing communication mix”. Management Science, 54(3), 477-491.
Cilibrasi, R. and Vitányi, P. M. B. (2007) “The google similarity distance”. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370-383.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K. and Kuksa, P. (2011) “Natural language processing (almost) from scratch”. Journal of Machine Learning Research, 12(Aug), 2493-2537.
Dellarocas, C., Zhang, X. M. and Awad, N. F. (2007) “Exploring the value of online product reviews in forecasting sales: The case of motion pictures”. Journal of Interactive Marketing, 21(4), 23-45.
Gil de Zúñiga, H., Jung, N. and Valenzuela, S. (2012) “Social media use for news and individuals’ social capital, civic engagement and political participation”. Journal of Computer-Mediated Communication, 17(3), 319-336.
Hofmann, T. (2017) “Probabilistic latent semantic indexing”. In: ACM SIGIR Forum, 51(2), 211-218.
Hu, M. and Liu, B. (2004) “Mining and summarizing customer reviews”. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 168-177.
Jakob, N. and Gurevych, I. (2010) “Extracting opinion targets in a single and cross-domain setting with conditional random fields”. In: Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing. 1035-1045.
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Jin, W. and Ho, H. H. (2009) “A novel lexicalized HMM-based learning framework for web opinion mining”. In: Proceedings of the 26th Annual International Conference on Machine Learning. 465-472.
Kim, H. D., Park, D. H., Lu, Y. and Zhai, C. (2012) “Enriching text representation with frequent pattern mining for probabilistic topic modeling”. Proc. of the American Society for Information Science and Technology, 49(1), 1-10.
Lafferty, J. D., Mccallum, A. and Pereira, F. C. N. (2001) “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”. In: Proceedings of the eighteenth International Conference on Machine Learning. 282-289.
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Moghaddam, S. and Ester, M. (2010) “Opinion digger: an unsupervised opinion miner from unstructured product reviews”. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 1825-1828.
Mukherjee, A. and Liu, B. (2012) “Modeling review comments”. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers. 1, 320-329.
Orengo, V. M. and Huyck, C. R. (2001) “A stemming algorithmm for the portuguese language”. In: Proceedings Eighth Symposium on String Processing and Information Retrieval. 186-193.
Park, D., Lee, J. and Han, I. (2007) “The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement”. International Journal of Electronic Commerce, 11(4), 125-148.
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S. and others (2016) “Semeval-2016 task 5: Aspect based sentiment analysis”. In: Proc. of the 10th International Workshop on Semantic Evaluation. 19-30. [GS Search]
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Qiu, G., Liu, B., Bu, J. and Chen, C. (2011) “Opinion word expansion and target extraction through double propagation”. Computational Linguistics, 37(1), 9-27.
Rabiner, L. R. (1990) “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”. In: Readings in speech recognition. Morgan Kaufmann. 267-296.
Rana T.A. and Cheah YN. (2018) “Improving Aspect Extraction Using Aspect Frequency and Semantic Similarity-Based Approach for Aspect-Based Sentiment Analysis”. In: Meesad P., Sodsee S., Unger H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. 317-326.
Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H. and Jin, C. (2007) “Red opal: product-feature scoring from reviews”. In: Proceedings 8th ACM Conference on Electronic Commerce. 182-191.
Sousa, R. F., Rabêlo, R. A. and Moura, R. S. (2015) “A fuzzy system-based approach to estimate the importance of online customer reviews”. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 1-8.
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. 417-424.
Yu, J., Zha, Z., Wang, M. and Chua, T. (2011) “Aspect ranking: Identifying important product aspects from online consumer reviews”. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. 1496-1505.
Wang, W., and Pan, S. J. (2019). “Syntactically-Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction”. Computational Linguistics, (Just Accepted), 1-32.
Aluisio, S. M., Pelizzoni, J. M., Marchi, A. R., Oliveira, L. de, Manenti, R. and Marquiafavel, V. (2003) “An account of the challenge of tagging a reference corpus for brazilian Portuguese”. In: Proceedings of International Workshop on Computational Processing of the Portuguese Language. 110-117.
Archak, N., Ghose, A. and Ipeirotis, P. G. (2007) “Show me the money!: deriving the pricing power of product features by mining consumer reviews”. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 56-65.
Asnani, K., and Pawar, J. D. (2018). Extraction of Code-mixed Aspect Topics in Semantic Representation. Computación y Sistemas, 22(1), 55-63.
Bickart, B. and Schindler, R. (2001) “Internet forums as influential sources of consumer information”. Journal of Interactive Marketing, 15(3), 31-40.
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003) “Latent dirichlet allocation”. Journal of Machine Learning Research, 3, 993-1022.
Bonchi, F., Castillo, C., Gionis, A. and Jaimes, A. (2011) “Social network analysis and mining for business applications”. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 22:1-22:37.
Branavan, S. R. K., Chen, H., Eisenstein, J. and Barzilay, R. (2009) “Learning document-level semantic properties from free-text annotations”. In: Journal of Artificial Intelligence Research, 34, 569-603.
Chen, Y. and Xie, J. (2008) “Online consumer review: Word-of-mouth as a new element of marketing communication mix”. Management Science, 54(3), 477-491.
Cilibrasi, R. and Vitányi, P. M. B. (2007) “The google similarity distance”. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370-383.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K. and Kuksa, P. (2011) “Natural language processing (almost) from scratch”. Journal of Machine Learning Research, 12(Aug), 2493-2537.
Dellarocas, C., Zhang, X. M. and Awad, N. F. (2007) “Exploring the value of online product reviews in forecasting sales: The case of motion pictures”. Journal of Interactive Marketing, 21(4), 23-45.
Gil de Zúñiga, H., Jung, N. and Valenzuela, S. (2012) “Social media use for news and individuals’ social capital, civic engagement and political participation”. Journal of Computer-Mediated Communication, 17(3), 319-336.
Hofmann, T. (2017) “Probabilistic latent semantic indexing”. In: ACM SIGIR Forum, 51(2), 211-218.
Hu, M. and Liu, B. (2004) “Mining and summarizing customer reviews”. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 168-177.
Jakob, N. and Gurevych, I. (2010) “Extracting opinion targets in a single and cross-domain setting with conditional random fields”. In: Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing. 1035-1045.
Jeon, S. W., Lee, H. J., Lee, H., and Cho, S. (2019, April). “Graph Based Aspect Extraction and Rating Classification of Customer Review Data”. In International Conference on Database Systems for Advanced Applications (pp. 186-199). Springer, Cham.
Jin, W. and Ho, H. H. (2009) “A novel lexicalized HMM-based learning framework for web opinion mining”. In: Proceedings of the 26th Annual International Conference on Machine Learning. 465-472.
Kim, H. D., Park, D. H., Lu, Y. and Zhai, C. (2012) “Enriching text representation with frequent pattern mining for probabilistic topic modeling”. Proc. of the American Society for Information Science and Technology, 49(1), 1-10.
Lafferty, J. D., Mccallum, A. and Pereira, F. C. N. (2001) “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”. In: Proceedings of the eighteenth International Conference on Machine Learning. 282-289.
Li, S., Zhou, L., and Li, Y. (2015). “Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures”. Information Processing & Management, 51(1), 58-67.
Liu, B. (2010) “Sentiment analysis and subjectivity”. In: Handbook of Natural Language Processing, 2(2010), 627-666.
Liu, B. (2012) “Sentiment Analysis and Opinion Mining”. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
Liu, Q., Gao, Z., Liu, B. and Zhang, Y. (2013) “A logic programming approach to aspect extraction in opinion mining”. In: IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). 276-283.
Long, C., Zhang, J. and Zhu, X. (2010) “A review selection approach for accurate feature rating estimation”. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. 766-774.
Milne, D. N. and Witten, I. H. (2013) “An open-source toolkit for mining Wikipedia”. Artificial Intelligence, 194, 222-239.
Moghaddam, S. and Ester, M. (2010) “Opinion digger: an unsupervised opinion miner from unstructured product reviews”. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 1825-1828.
Mukherjee, A. and Liu, B. (2012) “Modeling review comments”. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers. 1, 320-329.
Orengo, V. M. and Huyck, C. R. (2001) “A stemming algorithmm for the portuguese language”. In: Proceedings Eighth Symposium on String Processing and Information Retrieval. 186-193.
Park, D., Lee, J. and Han, I. (2007) “The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement”. International Journal of Electronic Commerce, 11(4), 125-148.
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S. and others (2016) “Semeval-2016 task 5: Aspect based sentiment analysis”. In: Proc. of the 10th International Workshop on Semantic Evaluation. 19-30. [GS Search]
Poria, S., Cambria, E. and Gelbukh, A. F. (2016) “Aspect extraction for opinion mining with a deep convolutional neural network”. Knowledge-Based Systems, 108. 42-49.
Porter, M. F. (1980) “An algorithm for suffix stripping”. Program.
Qiu, G., Liu, B., Bu, J. and Chen, C. (2011) “Opinion word expansion and target extraction through double propagation”. Computational Linguistics, 37(1), 9-27.
Rabiner, L. R. (1990) “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”. In: Readings in speech recognition. Morgan Kaufmann. 267-296.
Rana T.A. and Cheah YN. (2018) “Improving Aspect Extraction Using Aspect Frequency and Semantic Similarity-Based Approach for Aspect-Based Sentiment Analysis”. In: Meesad P., Sodsee S., Unger H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. 317-326.
Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H. and Jin, C. (2007) “Red opal: product-feature scoring from reviews”. In: Proceedings 8th ACM Conference on Electronic Commerce. 182-191.
Sousa, R. F., Rabêlo, R. A. and Moura, R. S. (2015) “A fuzzy system-based approach to estimate the importance of online customer reviews”. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 1-8.
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. 417-424.
Yu, J., Zha, Z., Wang, M. and Chua, T. (2011) “Aspect ranking: Identifying important product aspects from online consumer reviews”. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. 1496-1505.
Wang, W., and Pan, S. J. (2019). “Syntactically-Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction”. Computational Linguistics, (Just Accepted), 1-32.
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Published
2020-06-17
Como Citar
Vieira, J. P. A., & Moura, R. S. (2020). Análise de Métodos de Extração de Aspectos em Opiniões Regulares. ISys - Revista Brasileira De Sistemas De Informação, 13(3), 82–97. https://doi.org/10.5753/isys.2020.796
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Artigos regulares
