A Study on Text-Score Disagreement in Online Reviews

Fazzolari, Michela; Cozza, Vittoria; Petrocchi, Marinella; Spognardi, Angelo

doi:10.1007/s12559-017-9496-y

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…However, the study by Mudambi et al (2014) was based on a small data set of only 1,734 reviews on 23 products on Amazon. Fazzolari et al (2017) focused on individual inconsistent reviews on TripAdvisor using sentiment analysis. Their findings showed that 6% of 164,300 hotel reviews on TripAdvisor were inconsistent.…”

Section: Literature On Inconsistent Reviewsmentioning

confidence: 99%

“…Within the four treatments, all participants received five reviews for each of the four restaurants. Following Fazzolari et al (2017), we only focus on strong inconsistent reviews in our research. Hence, the quantitative component included only negative reviews (one or two out of five stars) and positive reviews (four or five out of five stars).…”

Section: Basic Setup and Treatmentsmentioning

confidence: 99%

“…For instance, 30% of Amazon's product reviews (Mudambi et al, 2014) were inconsistent. Also, multiple seller reviews on TripAdvisor (6%) (Fazzolari et al, 2017) and the German physician rating platforms jameda and DocInsider (more than 3%) (Geierhos et al, 2015) were inconsistent.…”

Section: Introductionmentioning

confidence: 99%

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It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms

2022

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Consumer-generated reviews play a decisive role in creating trust and facilitating transactions on digital platforms. However, prior research shows various problems, e.g., only a small number of consumers providing reviews, fake reviews, and inconsistent reviews. We use an experiment in the context of a restaurant booking platform to examine the impact of inconsistent reviews on the duration of consumers’ transaction decisions. In a second experiment, we investigate the relative importance of the review components in the case of inconsistent reviews. Drawing on the dual-process theory and media richness theory, we predict that inconsistent reviews result in a longer time required for consumers’ transaction decisions (H1) and lead to users’ transaction decisions being predominantly based on the qualitative component (H2). Although we do not find general support that inconsistent restaurant reviews negatively determine the duration of transaction decisions, we find evidence that in the case of inconsistent restaurant reviews, the polarity of the qualitative component is crucial for both the duration of the transaction decision and the decision itself.

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Section: Literature On Inconsistent Reviewsmentioning

confidence: 99%

Section: Basic Setup and Treatmentsmentioning

confidence: 99%

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It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms

2022

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“…It is worth noting that online reviews can present inconsistencies between the sentiment evaluation of the review texts and the correspondent user ratings, as pointed out in [67] and [68]. Thus, in order to achieve a high probability that the text of the reviews is coherent with the evaluation label, for each scenario, only the reviews at the extremes of the evaluation scale are selected.…”

Section: Experimental Setup: Details On the Labeled Training Setsmentioning

confidence: 99%

An effective Decision Support System for social media listening based on cross-source sentiment analysis models

Ducange

Fazzolari

Petrocchi

et al. 2019

Engineering Applications of Artificial Intelligence

Self Cite

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Nowadays, companies and enterprises are more and more incline to exploit the pervasive action of on-line social media, such as Facebook, Twitter and Instagram. Indeed, several promotional and marketing campaigns are carried out by concurrently adopting several social medial channels. These campaigns reach very quickly a wide range of different categories of users, since many people spend most of their time on on-line social media during the day.In this work, a Decision Support System (DSS) is presented, which is able to efficiently support companies and enterprises in managing promotional and marketing campaigns on multiple social media channels. The proposed DSS continuously monitors multiple social channels, by collecting social media users' comments on promotions, products, and services. Then, through the analysis of these data, the DSS estimates the reputation of brands related to specific companies and provides feedbacks about a digital marketing campaign.The core of the proposed DSS is a Sentiment Analysis Engine (SAE), which is able to estimate the users' sentiment in terms of positive, negative or neutral polarity, expressed in a comment. The SAE is based on a machine learning text classification model, which is initially trained by using real data streams coming from different social media platforms specialized in user reviews (e.g., TripAdvisor). Then, the monitoring and the sentiment classification are carried out on the comments continuously extracted from a set of public pages and channels of publicly available social networking platforms (e.g., Facebook, Twitter, and Instagram). This approach is labeled as cross-source sentiment analysis.After some discussions on the design and the implementation of the proposed DSS, some results are shown about the experimentation of the proposed DSS on two scenarios, namely restaurants and consumer electronics online shops. Specifically, first the application of effective sentiment analysis models, created relying on user reviews is discussed: the models achieve accuracies higher than 90%. Then, such models are embedded into the proposed DSS. Finally, the results of a social listening campaign are presented. The campaign was carried out by fusing data streams coming from real social channels of popular companies belonging to the selected scenarios.

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“…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%

Properties of feedback mechanisms on digital platforms: an exploratory study

Steur

Seiter

2020

J Bus Econ

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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.

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A Study on Text-Score Disagreement in Online Reviews

Cited by 11 publications

References 40 publications

It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms

It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms

An effective Decision Support System for social media listening based on cross-source sentiment analysis models

Properties of feedback mechanisms on digital platforms: an exploratory study

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