Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2020.14.1.4Keywords:
aspect-based sentiment analysis, aspect extraction, dependency rule, rule learning, sequential coveringAbstract
The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.
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References
Qiu, G., Liu, B., Bu, J. & Chen, C., Opinion Word Expansion and Target Extraction through Double Propagation, Computational Linguistics, 37(1), pp. 9-27, 2011.
Bancken, W., Alfarone, D. & Davis, J., Automatically Detecting and Rating Product Aspects from Textual Customer Reviews, Proceedings of Workshop on Interactions between Data Mining and Natural Language Processing (DMNLP), pp. 1-16, 2014.
Liu, Q., Gao, Z., Liu, B. & Zhang, Y., Automated Rule Selection for Aspect Extraction in Opinion Mining, IJCAI Int. Jt. Conf. Artif. Intell., pp. 1291-1297, 2015.
Ruskanda, F.Z., Widyantoro, D.H. & Purwarianti, A., Sequential Covering Rule Learning for Language Rule-based Aspect Extraction, Proceeding of 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS), pp. 229-234, 2019.
Jin, W. & Ho, H.H., A Novel Lexicalized HMM-based Learning Framework for Web Opinion Mining, Proceedings of the 26th Annual International Conference on Machine Learning - ICM, pp. 1-8, 2009.
Toh, Z. & Wang, W., DLIREC: Aspect Term Extraction and Term Polarity Classification System, Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), SemEval, pp. 235-240, 2014.
Jakob, N. & Gurevych, I., Extracting Opinion Targets in a Single-and Cross-domain Setting with Conditional Random Fields, Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1035-1045, 2010.
Yang, B. & Cardie, C., Joint Inference for Fine-grained Opinion Extraction, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1640-1649, 2013.
Samha, A.K., Li, Y. & Zhang, J., Aspect-based Opinion Mining from Product Reviews Using Conditional Random Fields, Data Mining and Analytics: Proceedings of the 13th Australasian Data Mining Conference, pp. 119-128, 2015.
Mitchell, M., Aguilar, J., Wilson, T. & Van Durme, B., Open Domain Targeted Sentiment, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), pp. 1643-1654, 2013.
Wu, C., Wu, F., Wu, S., Yuan, Z. & Huang, Y., A Hybrid Unsupervised Method for Aspect Term and Opinion Target Extraction, Knowledge-Based Systems, 148, pp. 66-73, 2018.
Zhang, L. & Liu, B., Aspect and Entity Extraction for Opinion Mining, in Data Mining and Knowledge Discovery for Big Data, Studies in Big Data, Berlin, Heidelberg: Springer, pp. 1-14, 2014.
Titov, I. & McDonald, R., Modeling Online Reviews with Multi-grain Topic Models, Proceedings of the 17th International Conference on World Wide Web, pp. 111-120, 2008.
Moghaddam, S. & Ester, M., On the Design of LDA Models for Aspect-based Opinion Mining, Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM '12, pp. 803-812, 2012.
Chen, Z., Mukherjee, A. & Liu, B., Aspect Extraction with Automated Prior Knowledge Learning, 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference, 1, pp. 347-358, 2014.
Mukherjee, A. & Liu, B., Aspect Extraction through Semi-supervised Modeling, ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1, July, pp. 339-348, 2012.
Wang, T., Cai, Y., Leung, H., Lau, R.Y.K., Li, Q. & Min, H., Product Aspect Extraction Supervised with Online Domain Knowledge, Knowledge-Based Systems, 71, pp. 86-100, 2014.
Hu, M. & Liu, B., Mining Opinion Features in Customer Reviews, American Association for Artificicial Intelligence, pp. 755-760, 2004.
Akhtar, M.S., Gupta, D., Ekbal, A. & Bhattacharyya, P., Feature Selection and Ensemble Construction: A Two-step Method for Aspect Based Sentiment Analysis, Knowledge-Based Systems, 125, pp. 116-135, 2017.
Wu, Y., Zhang, Q., Huang, X.X. & Wu, L., Phrase Dependency Parsing for Opinion Mining, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 3, pp. 1533-1541, 2009.
Nguyen, T.H. & Shirai, K., PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2509-2514, 2015.
Popescu, A.M. & Etzioni, O., Extracting Product Features and Opinions from Reviews, Proceedings of the Conference on Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 339-346, 2005.
Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.a. & Reynar, J., Building a Sentiment Summarizer for Local Service Reviews, WWW Workshop on NLP in the Information Explosion Era, pp. 339-348, 2008.
Luo, H., Li, T., Liu, B., Wang, B. & Unger, H., Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation, IEEE/ACM Transactions on Audio Speech and Language Processing, 27(7), pp. 1201-1212, 2019.
Liu, Q., Gao, Z., Liu, B. & Zhang, Y., Automated rule selection for opinion target extraction, Knowledge-Based Systems, 104, 2016.
Jindal, N. & Liu, B., Mining Comparative Sentences and Relations, AAAI'06 Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1331-1336, 2006.
Chao, C. & Jiang, W., Study on the Subjective and Objective Text Classification and Pretreatment of Chinese Network Text, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2, pp. 25-29, 2012.
Mitchell, T.M., Learning Set of Rules, in Machine Learning, McGraw-Hill, pp. 274-306, 1997.
Furnkranz, J., Gamberger, D., & Lavrac, N., Foundations of Rule Learning, Springer, 2012.
Michelson, M. & Macskassy, S., An Efficient Sequential Covering Algorithm for Explaining Subsets of Data, Proceedings of International Conference on Artificial Intelligence ICAI, pp. 261-267, 2010.
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Bing Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jimenez-Zafra, S.M. & EryiAYit, G., SemEval-2016 Task 5: Aspect Based Sentiment Analysis, Proceedings of SemEval-2016, pp. 19-30, 2016.
Chen, D. & Manning, C.D., A Fast and Accurate Dependency Parser using Neural Networks, Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP) , pp. 740-750, 2014.
Powers, D. M. W., Evaluation"i: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2(1), pp. 37-63, 2007.
Li, Q., Jin, Z., Wang, C. & Zeng, D. D., Mining Opinion Summarizations using Convolutional Neural Networks in Chinese Microblogging Systems, Knowledge-Based Systems, 107, pp. 289-300, 2016.
