Designing novel review ranking systems: predicting the usefulness and impact of reviews

By Anindya Ghose, Panagiotis G. Ipeirotis. International Conference on Evolutionary Computation, 2007

349 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/2be2f67d0369ea1b6b924ff499d84f7d7c10640d

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Citations in the corpus

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].”
How opinions are received by online communities: a case study on amazon.com helpfulness votes (2009, neutral): “determination of helpfulness, treating it as a classification or regression problem with Amazon helpfulness votes providing labeled data [10, 15, 17].”
Opinion Mining and Sentiment Analysis (2008, background): “[161], Zhang and Varadarajan [329], and Ghose and Ipeirotis [106] attempt to automatically”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, methodology): “In [20], [21] we examined just the effect of subjectivity, while in the current work, we expanding our data to include more product categories and examine a significantly increased number of features, such as different readability metrics,”
Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories (2012, background): “Experience goods, such as movies and music, seem to attract reviews from consumers who either love them or hate them, with extremely positive reviews especially common (Ghose and Ipeirotis 2007).”
How Community Feedback Shapes User Behavior (2014, background): “One major research direction is concerned with predicting the helpfulness ratings of product reviews starting from textual and social factors (Ghose and Ipeirotis 2007; Liu et al. 2007; Otterbacher 2009; Tsur and Rappoport 2009; Lu et al. 2010; quence of the actual textual quality of the post, but…”
Modeling and Predicting the Helpfulness of Online Reviews (2008, background): “" [1] [5] [11], and " How to understand and summarize the reviews with minimum human efforts?”
Sentiment Analysis and Subjectivity (2010, methodology): “A related problem that has also been studied in the past few years is the determination of the usefulness, helpfulness or utility of a review [31, 49, 57, 110].”
Estimating Aggregate Consumer Preferences from Online Product Reviews (2010, positive): “Based on an empirical study of the usefulness and impact of product reviews, Ghose and Ipeirotis (2007) suggested that customer opinions provide a meaningful basis for identifying those product attributes that are important for marketing purposes.”
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques (2012, background): “This may include consumer-oriented or manufacturer-oriented ranking mechanisms [18], depending on the given application domain, as well as external factors, such as social networks [31].”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “Subjectivity classification was also applied in [26].”