Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection

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dc.contributor.authorClaypo, Niphat
dc.contributor.authorJaiyen, Saichon
dc.date.accessioned2026-08-06T10:08:05Z
dc.date.available2026-08-06T10:08:05Z
dc.date.issued2014-01-01
dc.description.abstractCurrently, Thai restaurants are popular around the world. There are tons of reviews related to foods and services in social networking websites. These tons of customer reviews make it difficult to analyze the opinions of customer toward foods and services. To help the businesses, the model of opinion mining is proposed for classifying the reviews and to analyze the attitude of customers for improving their products and services. In this research, the artificial neural network is applied to classify the positive and negative reviews. In addition, the mRMR feature selection is used to select the features of data in order to reduce the number of features in the data set. Consequently, the computational times of learning algorithms for neural networks are reduced. The experimental results show that the neural network is an effective model for classifying the Thai restaurant reviews.
dc.identifier.citation2014 International Computer Science and Engineering Conference Icsec 2014, 394-397, 2014
dc.identifier.doi10.1109/ICSEC.2014.6978229
dc.identifier.other2-s2.0-84988268502
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5280
dc.source2014 International Computer Science and Engineering Conference Icsec 2014
dc.subjectClassification
dc.subjectFeature selection
dc.subjectMinimal-redundancy-maximal-relevance (mRMR)
dc.subjectMultilayer perceptron (MLP)
dc.subjectRadial basis function (RBF)
dc.subjectSupport Vector Machine (SVM)
dc.titleOpinion mining for Thai restaurant reviews using neural networks and mRMR feature selection
dc.typeConference Paper

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