Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets

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.

[1]  Huawen Liu,et al.  Multi-criteria decision-making methods based on intuitionistic fuzzy sets , 2007, Eur. J. Oper. Res..

[2]  Z. S. Xu,et al.  An overview of operators for aggregating information , 2003, Int. J. Intell. Syst..

[3]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[4]  Narasimhan Sundararajan,et al.  Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Pratyush Sen,et al.  Preference modelling by estimating local utility functions for multiobjective optimization , 1996 .

[6]  Zexuan Zhu,et al.  A fast pruned-extreme learning machine for classification problem , 2008, Neurocomputing.

[7]  Rui Xia,et al.  Ensemble of feature sets and classification algorithms for sentiment classification , 2011, Inf. Sci..

[8]  Bin Lin,et al.  Sentiment classification for Chinese reviews: a comparison between SVM and semantic approaches , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[9]  Vibhu O. Mittal,et al.  Comparative Experiments on Sentiment Classification for Online Product Reviews , 2006, AAAI.

[10]  Amaury Lendasse,et al.  Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction , 2009, ICANN.

[11]  Michal Wozniak,et al.  Method of classifier selection using the genetic approach , 2010, Expert Syst. J. Knowl. Eng..

[12]  Chrysanthos Dellarocas,et al.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003, Manag. Sci..

[13]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[14]  Zhan-Li Sun,et al.  A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Luís A. Alexandre,et al.  On combining classifiers using sum and product rules , 2001, Pattern Recognit. Lett..

[16]  Chee Kheong Siew,et al.  Can threshold networks be trained directly? , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[17]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[18]  Xue Bai,et al.  Predicting consumer sentiments from online text , 2011, Decis. Support Syst..

[19]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[20]  Tekmoloji.  Pacific Asia Conference on Information Systems , 2010 .

[21]  Jin Zhang,et al.  An empirical study of sentiment analysis for chinese documents , 2008, Expert Syst. Appl..

[22]  Han Wang,et al.  Ensemble Based Extreme Learning Machine , 2010, IEEE Signal Processing Letters.

[23]  Yuan Lan,et al.  Ensemble of online sequential extreme learning machine , 2009, Neurocomputing.

[24]  Yew-Soon Ong,et al.  Extreme learning machine for multi-categories classification applications , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[25]  Ludmila I. Kuncheva,et al.  Using measures of similarity and inclusion for multiple classifier fusion by decision templates , 2001, Fuzzy Sets Syst..

[26]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[27]  Hua Xu,et al.  An empirical study of unsupervised sentiment classification of Chinese reviews , 2010 .

[28]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[29]  Yong Yu,et al.  Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..

[30]  Qiang Ye,et al.  Sentiment classification of online reviews to travel destinations by supervised machine learning approaches , 2009, Expert Syst. Appl..

[31]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[32]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[33]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[34]  Gholam Ali Montazer,et al.  Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition , 2009, Artif. Intell. Medicine.

[35]  Keung-Chi Ng,et al.  Consensus diagnosis: a simulation study , 1992, IEEE Trans. Syst. Man Cybern..

[36]  Zili Zhang,et al.  Sentiment classification of Internet restaurant reviews written in Cantonese , 2011, Expert Syst. Appl..

[37]  Deyu Li,et al.  A feature selection method based on improved fisher's discriminant ratio for text sentiment classification , 2011, Expert Syst. Appl..

[38]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[39]  T. Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1999, ECML.