Abstract
There are a large number of subjective texts which contain people’s all kinds of sentiments and emotions in social media. Analyzing the sentiments and predicting the emotional expressions of human beings have been widely studied in academic communities and applied in commercial systems. However, most of the existing methods focus on single-label sentiment analysis, which means that only an exclusive sentiment orientation (negative, positive or neutral) or an emotion state (joy, hate, love, sorrow, anxiety, surprise, anger, or expect) is considered for a document. In fact, multiple emotions may be widely coexisting in one document, paragraph, or even sentence. Moreover, different words can express different emotion intensities in the text. In this paper, we propose an approach that combining fuzzy relation equation with fuzzy-rough set for solving the multi-label emotion intensity analysis problem. We first get the fuzzy emotion intensity of every sentiment word by solving a fuzzy relation equation, and then utilize an improved fuzzy-rough set method to predict emotion intensity for sentences, paragraphs, and documents. Compared with previous work, our proposed algorithm can simultaneously model the multi-labeled emotions and their corresponding intensities in social media. Experiments on a well-known blog emotion corpus show that our proposed multi-label emotion intensity analysis algorithm outperforms baseline methods by a large margin.
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References
Boutell, M., Luo, J., Shen, X., Christopher, M.: Brown: learning multi-label scene classification. Pattern Recogn. (PR) 37(9), 1757–1771 (2004)
Costa, E., Ferreira, R., Brito, P., et al.: A framework for building web mining applications in the world of blogs: a case study in product sentiment analysis. Expert Syst. Appl. (ESWA) 39(5), 4813–4834 (2012)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: NIPS 2001, pp. 681–687
Ernest, C., Józef, D., Witold, P.: Fuzzy relation equations on a finite set. Fuzzy Sets Syst. 7, 89–101 (1982)
Fürnkranz, J., Hüllermeier, E., Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. (ML) 73(2), 133–153 (2008)
Huang, S., You, J., Zhang, H., Zhou, W.: Sentiment analysis of Chinese micro-blog using semantic sentiment space model. In: Conference: Computer Science and Network Technology (ICCSNT), pp. 1443–1447 (2012)
Maji, P., Biswas, R., Roy, A.: Soft set theory. Comput. Math. Appl. 45(4–5), 555–562 (2003)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. CoRR cs.CL/0205070 (2002)
Pawlak, Z.: Rough sets. Int. J. Parallel Prog. (IJPP) 11(5), 341–356 (1982)
Quan, C., Ren, F.: A blog emotion corpus for emotional expression analysis in Chinese. Comput. Speech Lang. (CSL) 24(4), 726–749 (2010)
Shi, Z., Gong, Z.: The further investigation of covering-based rough sets: uncertainty characterization, similarity measure and generalized models. Inf. Sci. (ISCI) 180(19), 3745–3763 (2010)
Sun, B., Ma, W.: Soft fuzzy rough sets and its application in decision making. Artif. Intell. Rev. (AIR) 41(1), 67–80 (2014)
Vincenzo, L., Sabrina, S.: Knowl.-Based Syst. 58, 75–85 (2014)
Wang, C., Feng, S., Wang, D., Zhang, Y.: Fuzzy-rough set based multi-labeled emotion intensity analysis for sentence, paragraph and document. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC 2015. LNCS, vol. 9362, pp. 444–452. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25207-0_41
Wang, X., Zhai, J., Lu, S.: Induction of multiple fuzzy decision trees based on rough set technique. Inf. Sci. (ISCI) 178(16), 3188–3202 (2008)
Wu, W., Mi, J., Zhang, W.: Generalized fuzzy rough sets. Inf. Sci. (ISCI) 151, 263–282 (2003)
Wu, W., Zhang, W.: Constructive and axiomatic approaches of fuzzy approximation operators. Inf. Sci. (ISCI) 159(2), 233–254 (2004)
Xiao, Z., Yang, X., Pang, Y., Dang, X.: The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowl.-Based Syst. (KBS) 26, 196–206 (2012)
Zadeh, L.: Fuzzy sets. Inf. Control (IANDC) 8(3), 338–353 (1965)
Zhang, M., Zhou, Z.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. (TKDE) 26(8), 1819–1837 (2014)
Zhang, W., Liang, Y., Xu, P.: Uncertainty Reasoning Based on Inclusion Degree. Tsinghua University Press, Beijing (2007)
Zhang, W., Xu, H., Wan, W.: Weakness finder: find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Syst. Appl. (ESWA) 39(11), 10283–10291 (2012)
Acknowledgments
Project supported by National Natural Science Foundation of China (61370074, 61402091), the Fundamental Research Funds for the Central Universities of China under Grant N140404012.
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Wang, C., Wang, D., Feng, S., Zhang, Y. (2016). An Approach of Fuzzy Relation Equation and Fuzzy-Rough Set for Multi-label Emotion Intensity Analysis. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_6
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