Interpersonal conversation, or word-of-mouth (WOM), is one of the important factors in affecting product sales. WOM can not only increase product awareness among potential buyers but can also affect their buying decisions [8].With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information [1]. Due to large number of reviews for a single product, it is difficult for the customers to find the most useful reviews among such a large quantity and to find the true quality of the product. In this paper, we examine the reviews based on the textual characteristics in different time period to find the impact of reviews. To understand better the factors that influence consumers perception of usefulness and the factors that affect consumers the most, we conducted a two-level study. First, we performed an explanatory econometric analysis, trying to identify the aspects of a review that are important determinants of its usefulness and impact. Then, at the second level, we built a model using component weight assignment algorithm with SVM to pedict the most useful reviews.
Online user reviews have become an important source of information to consumers about the quality of various products. With the high volume of reviews that are typically published for a single product makes harder for individuals to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how different factors like subjectivity, readability, spelling errors, temporal pattern of online reviews, textual characteristics of online reviews in different time periods affect social outcomes like the extent of their perceived usefulness. Our elementary econometric analysis using two stage regression reveals that the extent of subjectivity, in formativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. By using component weight assignment algorithm along with SVM classifier we can accurately predict the impact of reviews on sales and their perceived usefulness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.