Analysis of Aspect Extraction Methods in Regular Opinions
DOI:
https://doi.org/10.5753/isys.2020.796Keywords:
Opinion Mining, Sentiment Analysis, Aspect ExtractionAbstract
This work presents a comparative analysis between the main approaches used at the task of Extraction of Aspects in reports about products and services on web sites. Adaptations of four methods of extraction of aspects were implemented and evaluated using two distinct Corpora, one in Portuguese and another in English. On the experiments performed it was observed that the approach using supervised learning (convolutional neural networks) obtained better results on the others.
Downloads
Download data is not yet available.
References
Agrawal, R. and Srikant, R. (1994) “Fast algorithms for mining association rules in large databases”. In: VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases. 487-499.
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.
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.
Downloads
Published
2020-06-17
How to Cite
Vieira, J. P. A., & Moura, R. S. (2020). Analysis of Aspect Extraction Methods in Regular Opinions. ISys - Brazilian Journal of Information Systems, 13(3), 82–97. https://doi.org/10.5753/isys.2020.796
Issue
Section
Regular articles
