DocumentCode
559685
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
fYear
2011
fDate
24-26 Oct. 2011
Firstpage
242
Lastpage
245
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;
fLanguage
English
Publisher
ieee
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
Type
conf
Filename
6108436
Link To Document