Title :
Sentiment classification of online product reviews using product features
Author :
Aleebrahim, Neda ; Fathian, Mohammad ; Gholamian, Mohammad Reza
Author_Institution :
Dept. of Electron. Commerce, Iran Univ. of Sci. & Technol., Tehran, Iran
Abstract :
There is a great number of online product reviews on the Internet which needs to be organized. In this paper, we consider the problem of sentiment classification of online reviews to determine the overall semantic orientation of customer reviews. Our proposed method for review classification is a supervised machine learning method based on extracting product features and the polarity of opinions expressed about the features.
Keywords :
Internet; Web sites; feature extraction; learning (artificial intelligence); pattern classification; Internet; customer review semantic orientation; online product reviews; opinion polarity; product feature extraction; product features; sentiment classification; supervised machine learning method; Data mining; Feature extraction; Itemsets; Semantics; Support vector machine classification; Vectors; customer reviews; product features; semantic orientation; sentiment classification;
Conference_Titel :
Data Mining and Intelligent Information Technology Applications (ICMiA), 2011 3rd International Conference on
Conference_Location :
Macao
Print_ISBN :
978-1-4673-0231-9
