Title :
Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection
Author :
Claypo, Niphat ; Jaiyen, Saichon
Author_Institution :
Dept. of Comput. Sci., King Mongkut´s Inst. of Technol. Ladkrabang, Bangkok, Thailand
fDate :
July 30 2014-Aug. 1 2014
Abstract :
Currently, 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.
Keywords :
catering industry; data mining; feature selection; learning (artificial intelligence); natural language processing; neural nets; pattern classification; social networking (online); Thai restaurant reviews; artificial neural network; computational times; customer review classification; learning algorithms; mRMR feature selection; opinion mining model; social networking Web sites; Accuracy; Artificial neural networks; Data mining; Support vector machines; Testing; Training; Classification; Feature selection; Support Vector Machine (SVM); minimal-redundancy-maximal-relevance (mRMR); multilayer perceptron (MLP); radial basis function (RBF);
Conference_Titel :
Computer Science and Engineering Conference (ICSEC), 2014 International
Conference_Location :
Khon Kaen
Print_ISBN :
978-1-4799-4965-6
DOI :
10.1109/ICSEC.2014.6978229
