Title :
Review analyzer: Analyzing consumer product reviews from review collections
Author :
Arun Manicka Raja, M. ; Winster, S.G. ; Swamynathan, S.
Author_Institution :
Dept. of Inf. Sci. & Technol., Anna Univ., Chennai, India
Abstract :
E-transactions have become prominent and highly convenient due to the widespread usage of the internet. The number of consumer reviews on various products is increasing day-by-day. These vast number of reviews are beneficial to manufacturers and consumers alike. It is a challenging task for a potential consumer to read all reviews to make a better purchase decision. It is beneficial to mine available consumer reviews for popular products from various consumer product review sites. The first step to do this is to decide the polarity of a review by performing sentiment analysis. We can then classify the review based on the polarity. This paper presents a comparison of a sentiment analyzer with other existing classifiers.
Keywords :
Internet; data mining; text analysis; Internet; Web text analysis; consumer product reviews; e-transactions; review analyzer; review collections; sentiment analysis; text mining; Consumer products; Crawlers; Data mining; Machine learning; Machine learning algorithms; Memory; XML; Information filtering; Information visualization; Text mining; Web Search; Web text analysis;
Conference_Titel :
Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4673-0252-4
DOI :
10.1109/RACSS.2012.6212682