Title :
Identifying the best feature combination for sentiment analysis of customer reviews
Author :
Priyanka, C. ; Gupta, Deepika
Author_Institution :
Dept. of Comput. Sci., Amrita Sch. of Eng., Bangalore, India
Abstract :
Opinions are increasingly available in form of reviews and feedback at websites, blogs, and microblogs which influence future customers. From human perspective, it is difficult to read all the opinions and summarize them which require an automated and faster opinion mining to classify the reviews. In this paper different features namely, N-gram features, POS based features and features based on the lexicon SentiWordNet, have been investigated. The Support Vector Machines (SVM) classifier has been modeled with presence as feature representation for classification of the reviews into positive and negative classes thereby identifying the best feature combination. Results of Experiments conducted on smart phone reviews for different feature combinations have been presented. A highest accuracy up till 92% and 95% has been obtained for small and large datasets, respectively.
Keywords :
Web sites; consumer behaviour; pattern classification; support vector machines; N-gram features; POS based features; SVM; Websites; best feature combination; customer reviews; future customers; lexicon SentiWordNet; microblogs; sentiment analysis; smart phone reviews; support vector machine classifier; Accuracy; Cameras; Feature extraction; Speech; Support vector machines; Tagging; Training; Machine Learning; Online reviews; Opinion mining; SentiWordNet; Sentiment analysis;
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on
Conference_Location :
Mysore
Print_ISBN :
978-1-4799-2432-5
DOI :
10.1109/ICACCI.2013.6637154
