Abstract
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 users’ 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%.
Highlights
The Internet offers an effective, global platform for Ecommerce, communication, and opinion sharing
We have demonstrated empirically that the approach proposed in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% outperforming the other two feature extraction strategies
We evaluated the effectiveness of the opinion mining strategy proposed in Section 3 at two tasks: (1) automatically identifying opinionated sentences based on extracted features and (2) classifying the polarity of the users’ opinions
Summary
The Internet offers an effective, global platform for Ecommerce, communication, and opinion sharing. On which people frequently express their opinions in natural language Mining through these terabytes of user review data is a challenging knowledgeengineering task. Our focus in this paper is efficient feature extraction, sentiment polarity classification, and comparative feature summary generation of online product reviews. Prior to making a purchase an online shopper typically browses through several similar products of different brands before reaching a final decision. We apply a multistep approach to the problem of automatic opinion mining that consists of various phases like Preprocessing, Semantic feature-set extraction followed by opinion summarization and classification. We have demonstrated empirically that the approach proposed in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% outperforming the other two feature extraction strategies. We conclude and discuss the scope for future work in this field
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