Exploring Latent Semantic Factors to Find Useful Product Reviews

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews

E-Word-of Mouth for Money—An Examination of the Impact of the Payment Timing on Knowledge Contribution Quality

Knowledge sharing is a determinant for online platforms to run their business. Several strategies have been proposed to increase knowledge sharing, among which monetary incentive is commonly used. While previous studies have found a crowding-out effect of monetary incentive on contribution quality, the research on alleviating this crowding out effect is limited. Drawing upon the autonomy in self-determination theory and the feeling of reciprocity, this study examines the impacts of payment timing of monetary incentives and contents in reminder messages sent by e-commerce platforms on contribution quality. This study intends to provide theoretical and practical implications about motivations on knowledge sharing

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Generating Personalised and Opinionated Review Summaries

Abstract. This paper describes a novel approach for summarising usergenerated reviews for the purpose of explaining recommendations. We demonstrate our approach using TripAdvisor reviews

An Investigation into Management Response to Negative Online Reviews in Hotel Operations

The advent of web2 and the interactivity it allowed net surfers to communicate freely with the purpose of exchanging ideas and opinions regarding the products they have purchased has given rise to a new marketing tool identified in the literature as consumer-generated content. As travel and hospitality are amongst the highest purchased services on the World Wide Web, a multitude of sites are currently made available to travelers to express either their satisfaction or voice their complaints on hotel properties that they have stayed at for business or pleasure. In providing informational queues, these online reviews are strongly affecting traveler’s pre-purchase decisions and their attitudes toward hotel choice. One factor that is considered of great effect on this decision-making process is identified as management response to reviews; regardless of their positive or negative nature. The purpose of this research is to provide an investigation into management’s behavior in responding to negative online reviews and the manner in which this type of feedback is handled in a way to build customer trust as well as a venue to service recovery.     Keywords: Customer-generated Content, Online Reviews, Travel, Hospitality, Management Response, Bahrai

La movilidad de los rankings de hoteles en Europa:El caso de Tripadvisor

La influencia que el ranking de hoteles de Tripadvisor ejerce en los turistas es reconocida tanto en el mundo académico como empresarial. Pese a la gran cantidad de autores que han analizado esta aplicación desde múltiples ángulos, nuestro trabajo se centra en un aspecto que todavía no ha sido estudiado: La evolución temporal del ranking. En sep/2015 iniciamos la extracción de las clasificaciones de los hoteles de las capitales europeas. En mar/2016 volvimos a extraer los rankings, comparando la evolución en el tiempo de más de 8.000 hoteles para responder entre otras a las siguientes cuestiones: ¿Cómo varía el ranking a lo largo del tiempo? ¿Los mejores hoteles en 2015 siguen siendo los mismos en 2016? ¿Hay grandes saltos en la clasificación? Los sorprendentes resultados obtenidos nos indican que Tripadvisor, como consecuencia de su enorme éxito y de su propio modo de establecer las valoraciones de los hoteles, está llegando al punto de saturación, al colapso.The influence that the Tripadvisor Hotels Ranking has on tourists when deciding where to stay is recognized by both the academic and business world. Despite the large number of authors who have analyzed this application from multiple angles, our work focuses on an aspect that is still little studied: Ranking Mobility. In Sep/2015 we started this work by collecting hotels rankings in the capitals of the 28 countries of the EU. In Mar/2016 we extracted again the rankings, comparing the evolution over time of the ranking positions of more than 8,000 European hotels to answer the following questions: How do the ranking positions evolve over time? Did the hotels best positioned in 2015 keep their positions in 2016? Is it the same at the bottom of the list? Have any of the hotels made a big jump up or down within the list? Our surprising results indicate that Tripadvisor’s hotel ranking, due to both its enormous success and its specific way to sort out hotels, is reaching saturation point

Rating Determinants Factored in E-Commerce Decision-Making

The user-generated content (UGC) Web sites are gaining popularity for a wide range of media content, such as news, blogs, forums, and open-source software. Instead of relying on information on company Web sites, users benefit by reading reviews written on UGC Web sites by consumers. Online evaluations are usually informative and reduce the information asymmetry. This study examines the problem where UGC can be expedient for online hotel booking. It investigates the relationship between the ratings obtained from the TripAdvisor.com reviewers and the hotel price levels in the United States, outside the United States, and top 20 hotels and others, respectively. Findings suggest that medium-priced hotels provide a comparable value with their high-priced counterparts. Further, the ratings for U.S. hotels are lower than others across all price levels

Reliability of reviews on the Internet : The case of TripAdvisor

Given the rampant growth of travel-related user-generated content on the Internet, this paper seeks to investigate the reliability of reviews in TripAdvisor, a popular travel review site. To support the goal, two objectives are submitted. The first is to locate clusters of highly-interlocked hotels that have been evaluated by a common pool of reviewers. This enables review baselines to be established, which in turn facilitates detection of anomalies. The second is to determine the inter-reviewer and intra-reviewer reliability of reviews. Results suggest that reviews in TripAdvisor could be largely reliable. The findings gleaned from the results are discussed

A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews

With the emergence of opinion spam, scholars in recent years have been investigating how to distinguish between authentic and fake online reviews. In this research area however, constructing ground truth has been a tricky problem. When labeled datasets of authentic and fake reviews are unavailable, it becomes impossible to systematically investigate differences between the two. In light of this problem, the goal of this paper is three-fold: (1) To review existing approaches of developing ground truth, (2) To present an improved methodological template to construct ground truth, and (3) To conduct a quality-check of the newly constructed ground truth. The existing approaches are dissected to identify several peculiarities. The new approach invests in mitigating pitfalls in the current approaches. In the newly constructed ground truth, authentic reviews were found to be not easily distinguishable from fake reviews. Finally, new research directions are identified with the hope that scholars would be able to stay ahead in their relentless race against spammers