Abstract
Binary classification is a critical process for opinion mining, which classifies opinions or user reviews into positive or negative classes. So far many popular binary classifiers have been used in opinion mining. The problematic issue is that there is a significant uncertain boundary between positive and negative classes as user reviews (or opinions) include many uncertainties. Many researchers have developed models to solve this uncertainty problem. However, the problem of broad uncertain boundaries still remains with these models. This paper proposes a three-way decision framework using semantic rules and fuzzy concepts together to solve the problem of uncertainty in opinion mining. This framework uses semantic rules in fuzzy concepts to enhance the existing three-way decision framework proposed by authors. The experimental results show that the proposed three-way framework effectively deals with uncertainties in opinions using relevant semantic rules.
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References
Ciullo, F., Zucco, C., Calabrese, B., Agapito, G., Guzzi, P.H., Cannataro, M.: Computational challenges for sentiment analysis in life sciences. In: 2016 International Conference on High Performance Computing and Simulation (HPCS), pp. 419–426. IEEE (2016)
Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al’.s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014)
Hughes, M., Li, I., Kotoulas, S., Suzumura, T.: Medical text classification using convolutional neural networks. Stud. Health Technol. Inform. 235, 246–50 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Subhashini, L.D.C.S., Li, Y., Zhang, J., Atukorale, A.: Integration of fuzzy and deep learning in three-way decisions. In: Proceedings of the 2020 IEEE International Conference on Data Mining Workshop. ICDMW 2020. IEEE (2020)
Lei, Z., Yang, Y., Yang, M., Liu, Y.: A multi-sentiment-resource enhanced attention network for sentiment classification. arXiv preprint arXiv:1807.04990 (2018)
Li, S.T., Tsai, F.C.: A fuzzy conceptualization model for text mining with application in opinion polarity classification. Knowl.-Based Syst. 39, 23–33 (2013)
Li, Y., Algarni, A., Albathan, M., Shen, Y., Bijaksana, M.A.: Relevance feature discovery for text mining. IEEE Trans. Knowl. Data Eng. 27(6), 1656–1669 (2015)
Li, Y., Algarni, A., Zhong, N.: Mining positive and negative patterns for relevance feature discovery. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 753–762. ACM (2010)
Li, Y., Zhang, L., Xu, Y., Yao, Y., Lau, R.Y.K., Wu, Y.: Enhancing binary classification by modeling uncertain boundary in three-way decisions. IEEE Trans. Knowl. Data Eng. 29(7), 1438–1451 (2017)
Liu, B.: Sentiment analysis and subjectivity. Handb. Nat. Lang. Process. 2, 627–667 (2010)
Nadali, S., Murad, M.A.: Fuzzy semantic classifier to determine the strength levels of customer product reviews. In: Proceedings of the International Conference on Advances in Computer Science and Application (2012)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-49257-7_25
Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: a survey on models and techniques. Expert Syst. Appl. 40(16), 6601–6623 (2013)
Poria, S., Cambria, E., Gelbukh, A., Bisio, F., Hussain, A.: Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Comput. Intell. Mag. 10(4), 26–36 (2015)
Samb, S.M.K., Kandé, D., Camara, F., Ndiaye, S.: Improved bilingual sentiment analysis lexicon using word-level trigram. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC), pp. 112–119. IEEE (2019)
Samha, A.K., Li, Y., Zhang, J.: Aspect-based opinion extraction from customer reviews. arXiv preprint arXiv:1404.1982 (2014)
Turney, P.D.: 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, pp. 417–424. Association for Computational Linguistics (2002)
Van Dijk, T.A.: Semantic discourse analysis. Handb. Discourse Anal. 2, 103–136 (1985)
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. arXiv preprint arXiv:1809.05679 (2018)
Zhong, N., Li, Y., Wu, S.T.: Effective pattern discovery for text mining. IEEE Trans. Knowl. Data Eng. 24(1), 30–44 (2012)
Acknowledgment
This work was supported in part by the Queensland University of Technology, Australia, University Grant Commission, Sri Lanka, and University of Colombo School of Computing, Sri Lanka.
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Subhashini, L.D.C.S., Li, Y., Zhang, J., Atukorale, A.S. (2020). Three-Way Framework Using Fuzzy Concepts and Semantic Rules in Opinion Classification. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_9
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