Title :
Statistical Features Identification for Sentiment Analysis Using Machine Learning Techniques
Author :
Kamal, Arman ; Abulaish, Muhammad
Author_Institution :
Dept. of Math., Jamia Millia Islamia, New Delhi, India
Abstract :
Due to increasing fascinating trend of using internet and online social media, user-generated contents are growing exponentially on the Web, containing users´ opinion on various products. In this paper, we have proposed a sentiment analysis system which combines rule-based and machine learning approaches to identify feature-opinion pairs and their polarity. The efficiency of the proposed system is established through experimentation over customer reviews on different electronic products.
Keywords :
Internet; consumer behaviour; data mining; electronic commerce; electronic products; feature extraction; knowledge based systems; learning (artificial intelligence); social networking (online); statistical analysis; Internet; customer reviews; electronic products; feature-opinion pairs; machine learning techniques; online social media; opinion mining; polarity; rule-based approaches; sentiment analysis system; statistical features identification; text mining; user-generated contents; users opinion; Computational linguistics; Context; Data mining; Feature extraction; Mutual information; Noise measurement; Semantics; Feature identification; Machine learning; Opinion mining; Sentiment analysis; Text Mining;
Conference_Titel :
Computational and Business Intelligence (ISCBI), 2013 International Symposium on
Conference_Location :
New Delhi
Print_ISBN :
978-0-7695-5066-4
DOI :
10.1109/ISCBI.2013.43
