%0 Journal Article
%T Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews
%A Mita K. Dalal
%A Mukesh A. Zaveri
%J Applied Computational Intelligence and Soft Computing
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/910706
%X The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of usersĄŻ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%. 1. Introduction The Internet offers an effective, global platform for E-commerce, communication, and opinion sharing. It has several blogs devoted to diverse topics like finance, politics, travel, education, sports, entertainment, news, history, environment, and so forth. on which people frequently express their opinions in natural language. Mining through these terabytes of user review data is a challenging knowledge-engineering task. However, automatic opinion mining has several useful applications. Hence, in recent years researchers have proposed approaches for mining user-expressed opinions from several domains such as movie reviews [1], political debates [2], restaurant food reviews [3], and product reviews [4¨C11], and so forth Generating user-query specific summaries is also an interesting application of opinion mining [12, 13]. Our focus in this paper is efficient feature extraction, sentiment polarity classification, and comparative feature summary generation of online product reviews. Nowadays, several websites are available on which a variety of products are advertised and sold. Prior to making a purchase an online shopper typically browses through several similar products of different brands before reaching a final decision. This seemingly simple information retrieval task actually involves a lot of feature-wise comparison and decision making, especially since all manufacturers advertise similar features and competitive prices for most products. However, most online shopping sites also allow
%U http://www.hindawi.com/journals/acisc/2013/910706/