Why Aren't the Stars Aligned? An Analysis of Online Review Content and Star Ratings

Mudambi, Susan M.; Schuff, David; Zhang, Zhewei

doi:10.1109/hicss.2014.389

Cited by 38 publications

(33 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This query can be qualitative, quantitative, or both. Qualitative feedback includes textual information, while quantitative feedback is given using a predefined scale level (Chevalier and Mayzlin 2006;Mudambi et al 2014).…”

Section: Design Characteristics Of Feedback Mechanisms On Digital Plamentioning

confidence: 99%

“…Moreover, such dysfunctional effects extend beyond the mechanism of eBay. For instance, manipulations such as inconsistent feedback occur within other feedback mechanisms (e.g., Amazon, TripAdvisor and Booking.com) (Fazzolari et al 2017;Mayzlin et al 2014;Mudambi et al 2014). These dysfunctional feedback mechanisms may lead to a decline in users' motivation to provide feedback (McDonald and Slawson 2002) and, as a result, may cause a negative attitude towards feedback (Abramova et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Properties of feedback mechanisms on digital platforms: an exploratory study

Steur

Seiter

2020

J Bus Econ

View full text Add to dashboard Cite

Many digital platforms implement feedback mechanisms as a means to control the behavior of their users. However, there is a lack of theoretical explanation regarding the interrelation between design characteristics of feedback mechanisms and their effects. In this study, we interpret feedback mechanisms as a specific type of management control to propose properties as a new theoretical perspective on this problem. Our exploratory study has two objectives. First, we analyze how digital platforms design their feedback mechanisms. Second, we examine to what extent feedback mechanisms comply with standards given in the management control literature for our newly introduced properties. Analyzing the 102 most widely used platforms in Germany, we find dominant patterns in nearly all design characteristics (e.g., query method, submission category and scale level). Furthermore, we find mixed compliance of feedback mechanisms with our introduced properties (e.g., low precision but high sensitivity and verifiability). For a deeper understanding of these results, especially the reasons for the design choices, we conduct 14 semi-structured expert interviews. We find simplicity and inspiration from other platforms to be dominant drivers for design choices.

show abstract

Section: Design Characteristics Of Feedback Mechanisms On Digital Plamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Properties of feedback mechanisms on digital platforms: an exploratory study

Steur

Seiter

2020

J Bus Econ

View full text Add to dashboard Cite

show abstract

“…Instead, in this paper, we consider the presence of a possible misalignment between the review text and the review score. As noticed in [1], such a misalignment could lead to the increase of the consumer cognitive processing costs, to sub-optimal purchase decisions, and, ultimately, to neutralize the utility of the review site. Here, we approach the issue under a different perspective and with the aim of positively exploiting hidden information that may exist, for those reviews featuring the disagreement between the text and the score.…”

Section: Introductionmentioning

confidence: 99%

A Study on Text-Score Disagreement in Online Reviews

et al. 2017

View full text Add to dashboard Cite

Background/Introduction. In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts.Methods. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score.Results. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars -those reviews ranked with middle scores include a mixture of positive and negative aspects.Conclusions. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset -that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.

show abstract

“…Moreover, there is a strong correlation between high star ratings and helpful reviews. Another study has investigated how misalignment between the star rating and the textual content of the review can lower the overall helpfulness of the review [81]. It found that misalignment between star rating and review text often occurs in reviews of experience goods and in reviews with high star ratings.…”

Section: Categorymentioning

confidence: 99%

A Survey of Quality Prediction of Product Reviews

Almagrabi¹,

Malibari²,

McNaught³

2015

ijacsa

View full text Add to dashboard Cite

Abstract-With the help of Web-2.0, the Internet offers a vast amount of reviews on many topics and in different domains. This has led to an explosive growth of product reviews and customer feedback, which presents the problem of how to handle the abundant volume of data. It is an expensive and time-consuming task to analyze this huge content of opinions. Therefore, the need for automated sentiment analysis systems is vital. However, these systems encounter many challenges; assessing the content quality of the posted opinions is an important area of study that is related to sentiment analysis. Currently, review helpfulness is assessed manually; however the task of automatically assessing it has gained more attention in recent years. This paper provides a survey of approaches to the challenge of identifying the content quality of product reviews.

show abstract

Why Aren't the Stars Aligned? An Analysis of Online Review Content and Star Ratings

Cited by 38 publications

References 33 publications

Properties of feedback mechanisms on digital platforms: an exploratory study

Properties of feedback mechanisms on digital platforms: an exploratory study

A Study on Text-Score Disagreement in Online Reviews

A Survey of Quality Prediction of Product Reviews

Contact Info

Product

Resources

About