Estimating the Socio-Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

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. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers 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 dierent factors aect social outcomes like the extent of their perceived usefulness. Our approach explores multiple aspects of review text, such as lexical, grammatical, semantic, and stylistic levels to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in inuencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences have a negative eect on