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

Abstract

Abstract-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 different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors 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 influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: 'reviewer-related' features, 'review subjectivity' features, and 'review readability' features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.

What makes a useful online review? Implication for travel product websites

“…For instance, Amazon.com provides a service that displays the top two most helpful, favourable, and critical reviews posted by online users in order to help its customers evaluate each displayed product easily. These useful votes are generally believed not only to be an indicator of review diagnosticity to separate the useful reviews from the rest (Mudambi & Schuff, 2010), but also to be a signalling cue for users to filter numerous reviews efficiently (Ghose & Ipeirotis, 2008). In other words, the useful information in a review may assist customers to evaluate the attributes of the service so as to build confidence in the source (Gupta & Harris, 2010).…”

“…To be more specific, the dependent variable is online review usefulness (PU) measured by counting the number of online users who voted that the reviews were useful in response to the reviews posted (Ghose & Ipeirotis, 2010;Jin, Lu & Shi, 2002). The independent variables, including the messenger's name, address, and real photo, are binary variables to be measured as '1' if they disclose information and '0' otherwise.…”

Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing

“…In a context in which evaluation objects are ideas represented through text, the readability of that text is critical, as readability has been defined as the ease of understanding or comprehension of text due to the style of writing (Klare 1963). Readability relates directly to the cognitive effort of understanding text (Ghose and Ipeirotis 2011). Existing research has shown that readability strongly influences human decision processes.…”

“…Existing research has shown that readability strongly influences human decision processes. For instance, Ghose and Ipeirotis (2011) show that higher readability of user-generated online reviews positively influences purchase decisions. Similarly, Tan et al (2014) found that readability moderates the effect between the presentation of financial reports and the investors' earning evaluations.…”

“…In this context, ideas are often poorly written, contain spelling errors, and are difficult to understand (Blohm et al 2011a, Ghose andIpeirotis 2011). During the phase of information evaluation, blurry mental representations of an idea may lead users to ignore important information cues or to misinterpret them.…”