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

By Anindya Ghose, Panagiotis G. Ipeirotis. IEEE Transactions on Knowledge and Data Engineering, 2011

1,434 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f8164392081ff16481b657049eb40ed847297c53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Sociology and Political Science

Citations in the corpus

Social Media, Traditional Media, and Music Sales (2014, background): “…positive ones; consumer word of mouth is more important for niche products; featured reviews and reviews posted by reputable reviewers are more impactful (Forman et al. 2008); and the actual text of reviews offers incremental explanatory power beyond the average ratings (Ghose and Ipeirotis 2011).”
Predicting the “helpfulness” of online consumer reviews (2017, background): “Ghose and Ipeirotis (2011) analyze many characteristics of review texts, such as spelling errors, readability, and subjectivity, and examined their impact on sales.”
What makes a great MOOC? An interdisciplinary analysis of student retention in online courses (2013, background): “Furthermore , we will study how different factors affect the popularity of MOOCs and the helpfulness ( Ghose and Ipeirotis 2011 ) of online MOOC reviews in order to reduce the search costs for students and enable them to make more informed decisions .”
Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation (2017, background): “…and practitioners that can help in correctly understanding consumer preferences and demand: for example, to predict financial performance or attempt to increase sales (e.g., Chevalier and Mayzlin 2006; Clemons et al. 2006; Liu 2006; Chau and Xu 2012; Ghose, and Ipeirotis 2011; Ye et al. 2011).”
Understanding the Impact of Online Reviews on Hotel Performance (2017, methodology): “…and occupancy performance data matched to the online reviews, we further contribute to the tourism literature as we advance previous studies that used proxies such as hotel room sales or booking data (Chevalier and Mayzlin 2006; Ghose and Ipeirotis 2011; Ye, Law, and Gu 2009; Ye et al. 2011).”
What makes a useful online review? Implication for travel product websites. (2015, methodology): “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).”
Asymmetric effects of online consumer reviews (2015, methodology): “One of the dependent variables, online review usefulness, was measured by counting the number of online users who voted that the reviews were useful in response to the posted reviews (Ghose & Ipeirotis, 2011).”
Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales (2014, methodology): “Using subjectivity analysis, Ghose and Ipeirotis [18] found that the writing style of text reviews (subjectivity levels, readability, and extent of spelling errors) impacted product sales even in the presence of valence and volume of reviews in some of the product categories.”
A review of natural language processing techniques for opinion mining systems (2017, background): “In opinion spam detection and usefulness measurement, various related information about reviewers, raters, and products is taken into account together [16, 17, 18, 19, 20, 21, 22].”
Social Influence Effects in Online Product Ratings (2012, methodology): “To examine the robustness of the results to the choice of excluded variable used in the endogeneity correction, we estimated an alternative model using the Gunning Fog (readability) index (Ghose and Ipeirotis 2010) instead of the FKI as the excluded variable in Equations 2a–2d.”
When do consumers buy online product reviews? Effects of review quality, product type, and reviewer's photo (2014, background): “Also, Ghose and Ipeirotis (2011) analyzed actual product reviews on Amazon.com and found that highly subjective reviews were rated as less helpful when they concerned search goods (e.g., digital camera), but such negative effect of review subjectivity was attenuated in the case of reviews for…”
Helpfulness of Online Consumer Reviews: Readers' Objectives and Review Cues (2012, background): “However, only a few studies, including those by Ghose and Ipeirotis [19] and by Mudambi and Schuff [39], have investigated the factors that determine which kind of reviews are regarded as helpful.”