Modeling and Predicting the Helpfulness of Online Reviews
By Yang Liu, Xiangji Huang, Aijun An, Xiaohui Yu. 2008 Eighth IEEE International Conference on Data Mining, 2008
313 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/40bf161749414cff254db75f542830d81d9e4c8b
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
- Anindya Ghose
- Nripendra P. Rana
- Panagiotis G. Ipeirotis
- Yogesh Kumar Dwivedi
- Qiang Ye
- R. Law
- Bin Fang
- D. Kucukusta
In Information Systems
- Anindya Ghose
- Panagiotis G. Ipeirotis
- R. Law
- Qiang Ye
- Bin Fang
- D. Kucukusta
- Huan Sun
- Nripendra P. Rana
In Statistical and Nonlinear Physics
Citations in the corpus
- Analysis of the Perceived Value of Online Tourism Reviews: Influence of Readability and Reviewer Characteristics (2016, background): “By mining IMDB reviews, Liu et al. (2008) found that reviewer expertise and writing style influence review helpfulness received.”
- 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].”
- Importance of Online Product Reviews from a Consumer's Perspective (2013, methodology): “[1], who built and evaluated algorithms for predicting the usefulness of IMDB movie reviews.”
- Synthetic review spamming and defense (2013, background): “Research studies such as [15, 16] focus on classifying reviews as helpful (considered as high quality) or unhelpful (considered as low quality) based on human judgements.”
- Predicting the “helpfulness” of online consumer reviews (2017, background): “However, (Liu et al., 2008; Otterbacher, 2009) proposed a model for predicting the helpfulness of reviews using many features, such as length of reviews and the writing style of the reviewers.”
- Mining customer requirements from online reviews: A product improvement perspective (2016, background): “(2008) presented a non-linear regression model for to predict helpfulness and demonstrate that the proposed approach is highly effective [17].”
- Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, background): “From a business perspective, consumer product reviews are most influential if they affect product sales and the online behavior of users of the word-of-mouth forum.”