Abstract
Predicting consumer sentiments revealed in online reviews is crucial to suppliers and potential consumers. We combine online sequential extreme learning machines (OS-ELMs) and intuitionistic fuzzy sets to predict consumer sentiments and propose a generalized ensemble learning scheme. The outputs of OS-ELMs are equivalently transformed into an intuitionistic fuzzy matrix. Then, predictions are made by fusing the degree of membership and non-membership concurrently. Moreover, we implement ELM, OS-ELM, and the proposed fusion scheme for Chinese reviews sentiment prediction. The experimental results have clearly shown the effectiveness of the proposed scheme and the strategy of weighting and order inducing.
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Alexandre LA, Campilho AC, Kamel M (2001) On combining classifiers using sum and product rules. Pattern Recogn Lett 22(12):1283–1289
Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Set Syst 20(1):87–96
Bai X (2011) Predicting consumer sentiments from online text. Decis Support Syst 50(4):732–742
Chang C, Lin C (2001) Libsvm: a library for support vector machines. In: http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cheung C, Shek S, Sia C (2004) Virtual community of consumers:why people are willing to contribute? In: Proceeding of the 8th Pacific-Asia conference on information systems, pp 2100–2107
Cui H, Mittal V, Datar M (2006) Comparative experiments on sentiment classification for online product reviews. In: Proceedings of the twenty-first national conference on artificial intelligence(AAAI). Boston
Dellarocas C (2003) The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Manage Sci 49(10):1407–1424
Heeswijk M, Miche Y, Lindh-Knuutila T, Hilbers P, Oja THE, Lendasse A (2009) Adaptive ensemble models of extreme learning machine for time series prediction. In: International conference on artificial neural networks (ICANN2009), vol. 5769, pp. 305–314
Huang GB, Zhu QY, Mao KZ, Siew CK, Saratchandran P, Sundararajan N (2006) Can threshold networks be trained directly?. IEEE Trans Circuits Syst II 53(3):187–191
Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing 70
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: A new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), vol 2, pp 985–990. Budapest, Hungary (25–29 July, 2004)
Jackowski K, Wozniak M (2010) Method of classifier selection using the genetic approach. Expert Syst 27(2)
Joachims T, Nedellec C, Rouveirol C (1998) Text categorization with support vector machines: learning with many relevant. In: Proceedings of the European conference on machine learning, vol 1398. Dortmund, Germany, pp 137–142
Khatibi V, Montazer GA (2009) Intuitionistic fuzzy set vs fuzzy set application in medical pattern recognition. Artif Intell Med 47(1):43–52
Kuncheva L (2001) Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Set Syst 122(3):401–407
Kuncheva L, Bezdek J, Duin R (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn 34(2):299–314
Lan Y, Soh Y, Huang G (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72:3391–3395
Lewis D (1998) Naive (bayes) at forty: the independence assumption in information retrieval. Lect Note Comput Sci 1398:4–18
Liang N, Huang G, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423
Liu HW, Wang GJ (2007) Multi-criteria decision-making methods based on intuitionistic fuzzy sets. Eur J Oper Res 179(1):220–233
Liu N, Wang H (2010) Ensemble based extreme learning machine. IEEE Signal Process Lett 17(8):754–757
Ng K, Abramson B (1992) Consensus diagnosis: An simulation study. IEEE Trans Syst Man Cybern B Cybern 22(5):916–928
Rong H, Huang GB, Ong Y (2008) Extreme learning machine for multi-categories classification applications. In: 2008 international joint conference on neural networks (IJCNN2008), Hong Kong, pp 1709–1713
Rong H, Ong Y, Tan A, Zhu Z (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72:359–366
Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern B Cybern 39(4):1067–1072
Sun Z, Au K, Choi T (2007) A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines. IEEE Trans Syst Man Cybern B Cybern 37(5):1321–1331
Sun Z, Choi T, Au K, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419
Tan S, Zhang J (2008) An empirical study of sentiment analysis for chinese documents. Expert Syst Appl 34(4):2622–2629
Tan SB (2010) Chinese sentiment corpuses. In: http://www.searchforum.org.cn/tansongbo/corpus-senti.htm
Wang S, Li D, Song X, Wei Y, Li H (2011) A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Syst Appl 38(7):8696–8702
Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181(6):1138–1152
Xu Z (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187
Xu Z, Da Q (2003) An overview of operators for aggregating information. Int J Gen Syst 18(9):953–969
Yang J, Sen P (1996) Preference modelling by estimating local utility functions fro multiobjective optimisation. Eur J Oper Res 95(1):115–138
Ye Q, Lin B, Li Y (2005) Sentiment classification for chinese reviews: A comparison between svm and semantic approaches. In: Proceedings of the fourth international conference on machine learning and cybernetics (ICMLC2005), vol. 4
Ye Q, Zhang Z, Law R (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl 36(3):6527–6535
Zhang Z, Ye Q, Zhang Z, Li Y (2011) Sentiment classification of internet restaurant reviews written in cantonese. Expert Syst Appl 38(6):7674–7682
Zhou Z, Wu J, Tang W (2002) Ensembling neural networks: Many could be better than all. Artif Intell 137:239–263
Zhu Q, Qin A, Suganthan P, Huang G (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763
Zhu QY, Huang GB (2004) Source codes of ELM algorithm. In: http://www.ntu.edu.sg/home/egbhuang/. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Zhai ZW, H, Xu PJ (2010) An empirical study of unsupervised sentiment classification of chinese reviews. Tsinghua Sci Technol 15(6): 702–708
Acknowledgments
The authors thank the Guest Editors and two anonymous reviewers for their valuable comments and suggestions, which helped improve the paper greatly. This work was supported by the University Science Research Project of Jiangsu Province under Grant 11KJD630001.
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Wang, H., Qian, G. & Feng, XQ. Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets. Neural Comput & Applic 22, 479–489 (2013). https://doi.org/10.1007/s00521-012-0853-1
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DOI: https://doi.org/10.1007/s00521-012-0853-1