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Performance Analysis of Classification Methods for Opinion Mining

Opinion mining extracts opinions expressed by a specified target from a set of documents. Opinion mining determines sentiment orientation positive, negative, or neutral of a (short) text and is a text understanding technology assisting people locate relevant opinions in a review collection of large volumes, quickly. A search engine based on opinion mining technology reveals potential to address the above dilemma. An opinion-mining tool extracts opinion units and saves them in an opinion database. The collection of ordered information through the opinion search engine is considered opinion based rather than document based one. Hence, product review information is easily accessed. Recently, opinion mining has received many attentions due to many forums, blogs, e-commerce web sites, news reports and additional web sources where people express opinions. Some opinion mining tasks try to classify detected opinion using various scales. The main objective of this method is opinion identification in a text and then to classify them as positive, negative or neutral. In this paper, it is proposed to extract the feature set based on IDF (Inverse Document Frequency) and classification models based on Naive Bayes, RIDOR and FURIA.