User-generated Online Reviews Research Articles

Landscape values of large-scale civic infrastructure from visitors’ spatial perception: a content analysis

Many studies have investigated the improvement and evaluation of urban landscape development. However, only a few have discussed how to implement and align landscape values embedded in large-scale civic infrastructures, including bridges, railways, and public buildings. Taking the case of four typical large-scale civic structures, including three bridge sites and a dam project, this study explores how the landscape values embedded in these kinds of structures, whether in urban or suburban areas, are perceived by external visitors with different cultural backgrounds. Content analysis combining a quantitative approach with online user-generated reviews of on-site visits is applied and implemented. The interrelated effect of different spatial characteristics on the landscape values in those civic infrastructures is examined. The findings provide insights into efficient strategies to exploit various kinds of civic structures, especially as a promising tourism attraction for domestic and foreign markets, to achieve a more sustainable vision of public spaces in urban or broader contexts.

Tree-based data filtering for online user-generated reviews

Analysis of online user-generated reviews has attracted extensive attention with broad applications in recent years. However, the high-volume and low-value density of online reviews bring challenges to a timely and effective data utilization. To address that challenge, this work proposes an unsupervised review filtering method based on the inherent tree-structured hierarchies among review data that reflect the general-to-specific characteristics of various quality aspects discussed in reviews. In particular, the reviews with aspects distributed near the leaf nodes of the tree are capable of providing more specific and detailed information about the examined product, which is more likely to be reserved after the tree-based filtering. To enable an effective extraction of aspect hierarchies from a broad variety of review corpora, a Bayesian nonparametric hierarchical topic model has been constructed and incorporated with an enhanced Pólya urn scheme. The approximate inference of model parameters is obtained by an efficient collapsed Gibbs sampling procedure. The proposed method can enhance the layered effect of individual reviews according to their general-to-specific characteristics and reserve an information-rich subset filtered from the raw review corpus. The merits of the proposed method have been elaborated by case studies on two real-world data sets and an extensive simulation study.

Collusive anomalies detection based on collaborative markov random field

Abnormal collusive behavior, widely existing in various fields with concealment and synergy, is particularly harmful in user-generated online reviews and hard to detect by traditional methods. With the development of network science, this problem can be solved by analyzing structure features. As a graph-based anomaly detection method, the Markov random field (MRF)-based model has been widely used to identify the collusive anomalies and shown its effectiveness. However, existing methods are mostly unable to highlight the primary synergy relationship among nodes and consider much irrelevant information, which caused poor detectability. Therefore, this paper proposes a novel MRF-based method (ACEagle), considering node-level and community-level behavior features. Our method has several advantages: (1) based on the analysis of the nodes’ local structure, the community-level behavioral features are combined to calculate the nodes’ prior probability to close the ground truth, (2) it measured the behavior’s collaborative intensity between nodes by time and weight, constructing MRF by the synergic relationship exceeding the threshold to filter irrelevant structural information, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available. Through experiments in user-reviewed datasets where abnormal collusive behavior is most typical, the results show that ACEagle is significantly outperforming state-of-the-art baselines in collusive anomalies detection.

User-generated reviews and the financial performance of restaurants

PurposeThis paper aims to examine the impact of selected characteristics (rating, volume and variability) of online user-generated reviews on the bottom-line profitability of restaurants.Design/methodology/approachRestaurant-level review data are extracted from TripAdvisor and matched with firm-level data from the financial reports gathered from the Belfirst database of Bureau van Dijk. The resulting sample contains data on 2,297 Belgian firms over the period 2007–2018, for which 134,831 reviews are investigated. The author’s regression model of firm-level profitability is estimated against online review characteristics and various financial control variables, including past profitability. This research model and estimation technique address the endogeneity concerns that typically weaken this kind of study.FindingsWhile comparable studies on hotels document a positive association between review characteristics and profitability, the authors find no relationship between review rating, volume and variability in the profitability of restaurants.Research limitations/implicationsDue to the format of the financial reports of small and medium-sized enterprises (SMEs), data on turnover and cost of materials/services was not available for most restaurants in the sample, limiting our potential for analysis. In addition, our assessment of electronic word of mouth (eWOM) was limited to measures derived from user-generated reviews on TripAdvisor.Practical implicationsIn the literature on eWOM, the importance of online reputation is hardly disputed, especially in the context of the hospitality sector. However, most research to date has focused on the hotel sector and top-line measures of success. This study uses restaurant-level financial data, focuses on bottom-line profitability, considers potential endogeneity issues and pays careful attention to the estimation technique. The results fail to establish a direct relationship between eWOM metrics and financial performance and are surprising, meriting further investigation to establish the underlying causes.Originality/valueIn contrast to prior studies on the impact of eWOM on restaurant performance at a group level, this study examines the impact on unit-level profitability, taking into account several potential sources of estimation bias. In addition, the authors challenge this finding with a battery of sensitivity tests, revalidating the absence of a relationship in each case.

User Perception of Public Parks: A Pilot Study Integrating Spatial Social Media Data with Park Management in the City of Chicago

User-generated content (UGC) is a relatively young field of research; however, it has been proven useful in disciplines such as hospitality and tourism, to elicit public opinions of place usage. In landscape architecture and urban planning, UGC has been used to understand people’s emotions and movement in a space, while other areas and additional functions are yet to be discovered. This paper explores the capability of UGC in revealing city-scale park management problems and the applicability of social media as a future tool in bridging visitor feedback to city parks and recreation department staff. This research analyzed the spatial characteristics and patterns of Google Maps review quantity, rating score, and review comments. The results of this pilot study indicate the spatial and structural features of the Chicago parks and demonstrate distribution problems, financial investment priority concerns, park usage characteristics, and user preferences of the park attributes. Findings affirm that user-generated online reviews can be used as an alternative and self-reporting data source to effectively assess the natural performance and users’ experience of city parks and can potentially serve as an evaluative tool for public park management.

Determining banking service attributes from online reviews: text mining and sentiment analysis

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

The Contribution of Online Reviews for Quality Evaluation of Cultural Tourism Offers: The Experience of Italian Museums

In the cultural tourism field, there has been an increasing interest in adopting data-driven approaches that are aimed at measuring the service quality dimensions through online reviews. To date, studies measuring quality dimensions in cultural tourism settings through content analysis of online user-generated reviews are mainly based on manual approaches. When the content analysis is automated, these studies do not compare different analytical approaches. Our paper enters this field by comparing two different automated content analysis approaches to evaluate which of the two is more adequate for assessing the quality dimensions through user-generated reviews in an empirical setting of 100 Italian museums. Specifically, we compare a ‘top-down’ content analysis approach that is based on a supervised classification built on policy makers’ guidelines and a ‘bottom-up’ approach that is based on an unsupervised topic model of the online words of reviewers. The resulting museum quality dimensions are compared, showing that the ‘bottom-up’ approach reveals additional quality dimensions compared with those obtained through the ‘top-down’ approach. The misalignment of the results of the ‘top-down’ and ‘bottom-up’ approaches to quality evaluation for museums enhances the critical discussion on the contribution that data analytics can offer to support decision making in cultural tourism.

Opinion Fraud Detection in Online Reviews by Network Effects

User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.

Fake reviews on online platforms: perspectives from the US, UK and EU legislations.

User-generated online reviews have become essential sources of information for potential consumers. Given the increasing importance of online reviews on consumer purchases, some traders try to publish or promote fake reviews on online platforms to improve the reputation of their goods or services, or to damage the reputation of their competitors. This paper aims (1) to analyse whether and how fake reviews violate the main legal and ethical principles of advertising, according to the legislations of the US, UK and EU as well as the International Advertising and Marketing Communications Code; (2) to assess how the EU, the UK and the US are dealing with them, and find out which approaches and actions are working best; and (3) to offer normative recommendations for effective prosecution of fake reviews. The paper uses a combination of theoretical and sociological approaches (section 1), black letter analysis of law (sections 2 and 3) and a comparative approach (sections 2 and 3). Among others, the article observes (1) that fake reviews are a form of illegal advertising and (2) that the European regulatory instruments provide more protection against fake reviews than the US and argues (3) that an effective fight against fake reviews requires strong administrative bodies, endowed with sufficient resources.

The role of cultural heritage in visitor narratives of peatlands: analysis of online user-generated reviews from three peatland sites in England

ABSTRACT User-generated reviews of visitor attractions, on publicly available websites, such as Tripadvisor, are frequently used in tourism research but feature less often in published cultural heritage research. In this paper, we describe a qualitative analysis of the text from user-generated reviews of three peatland heritage landscapes in the United Kingdom – Ilkley Moor, Thorne and Hatfield Moors, and Shapwick Heath – to better understand the role tangible and intangible cultural heritage play in visitor perceptions and narratives of these sites. Our analysis indicates that visitors tend to emphasise natural over cultural heritage of peatland landscapes and hold plural, highly contextual and sometimes dissonant perceptions; there is no single story of peatlands. This presents both challenges and opportunities for building public appreciation of peatland cultural heritage. User-generated reviews offer, as-yet under-explored, potential data for use by heritage researchers and managers who seek to explore how visitors understand and use sites, and may also contribute to the emerging intangible heritage of heritage landscapes.

Customer Online Feedback with an Identity Versus No Identity: The Influence on Review Comments

As customers’ consumption is increasingly dominated by technology-driven systems, online self-verification becomes an important aspect of customers’ online purchasing behavior and plays a significant role in shaping social interactions in the online community. Across two studies, we examine whether online self-verification with an identity versus without an identity will lead to the different quality of online reviews. Study 1 used topic modeling with actual data stripped from Facebook and TripAdvisor customer online review sites and showed no difference between customer reviews underpinned with an identity or without. Likewise, Study 2 used an experimental design and found no significant difference between customer reviews with or without an identity. However, significant mediation effects of social ties and social capital were found when measuring the relationship between online self-verification and customer reviews. The findings build on the literature of user-generated online reviews and have important implications for academics and hospitality practitioners.

Multi-task learning based on question\u2013answering style reviews for aspect category classification and aspect term extraction on GPU clusters

Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.

Location in location-less environments: The role of geospatial concordance in online information evaluation

In spite of the capacity for the Internet to connect people and information irrespective of geography, physical location may paradoxically provide influential indicators of the perceived expertise of strangers and the credibility of the information they provide that may in turn guide people’s behaviors. To address this, this study examined the novel concept of geospatial concordance or the degree to which entities implicated in the sharing of aggregated opinions in online information pools are physically close to each other in geographic space. Predictions were tested in the context of user-generated online reviews using stimuli reflecting various types of geospatial concordance: between information consumers and online reviewers, between reviewed venues and their reviewers, and between consumers and reviewed venues. Findings support geographic perspectives emphasizing space as a mental construction imbued with particular meaning and confirm psychological views that people mentally construe places at different levels of abstraction, depending on their psychological, and physical, distance from them.

Examining the role of the marketing activity and eWOM in the movie diffusion: the decomposition perspective

Advertising as a direct marketing activity as well as word-of-mouth (WOM) as an indirect marketing activity are widely accepted as the most influential determinants of new product performance. Although electronic WOM (eWOM), as a type of WOM, has recently been studied extensively in various industries, previous results appear mixed due to their characteristics such as volume and valence. To bridge the gap regarding the roles of advertising and eWOM in the movie diffusion process, they were classified into pre-eWOM/advertising and post-eWOM/advertising based on two stages of the diffusion process. To reflect the heterogeneity of consumption characteristics on a new product, consumers were divided into two groups—innovators and imitators. This study proposed a model to investigate the role of advertising and eWOM in the movie diffusion process. In addition, the proposed model was used to analyze the impact of firm-initiated advertising and user-generated online reviews on movie performance in Korea.

Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision

User-generated online reviews are crucial for consumer decision-making, but suffer from under- provision, quality degradation, and imbalances across products. This research investigates whether “friend contributions cues,” in the form of highlighted reviews written by online friends, can motivate users to write more and higher-quality reviews. Noting the public-good nature of online reviews, we draw upon theories of pure altruism and competitive altruism to understand the effects of friend-contribution cues on review provision. We test our hypotheses using data from Yelp, and find positive effects of friend contribution cues. Users are three times more likely to provide a review after a recent friend review than after a recent stranger review, and this effect cannot be solely explained by homophily. Furthermore, reviews written after a friend’s review tend to be of higher quality, longer, and more novel. In addition, friend reviews tend to have a stronger effect on less-experienced users and less-reviewed products/services, suggesting friend- contribution cues can help mitigate the scarcity of contributions on “long-tail” products and from infrequent contributors. Our findings hold important implications for research and practice in the private provision of online reviews.

Exploring influential factors affecting guest satisfaction

探究影响顾客满意度的因素：基于消费者评论的大数据和商务分析研究目的以消费者评论为主体的社交网络口碑营销对于影响消费者决策和提高服务提供商的品牌形象、销量、和服务创新起到重要作用。然而, 很少研究探索社交媒体上的真正酒店客人评论。因此, 商务分析技术在文献中还是很少使用的, 这种技术应该更多得到科研上的应用以给酒店从业人员给与启示。因此, 本论文旨在探究影响酒店顾客满意度的因素, 通过消费者评论和商务分析, 以展示商务分析技术是如何为酒店业和科研界来使用的。研究设计/方法/途径本论文使用大数据和商务分析技术来进行数据分析。大数据和商务分析能够为酒店从业人员开发有效战略以提高产品和服务质量, 最后达到顾客满意。因此, 本论文分析了Tripadvisor.com的200, 431酒店评论数, 通过商务分析技术, 以探索和审视影响顾客满意度的重要因素。研究结果研究结果显示服务、客房、和价值比成为影响顾客满意度的前三项因素。品牌类型和负面情绪是影响顾客满意度的负面因素。其他因素成为影响顾客满意度的正面因素。研究原创性/价值本论文是利用消费者评论的商务分析来探究影响顾客满意度与具体衡量因素之间关系的起点范例, 以此, 帮助酒店从业商来解决服务中的欠缺因素, 提高绩效。

User acceptance and adoption of circular offerings in the fashion sector: Insights from user-generated online reviews

Recent research on circular offerings has indicated that the lack of consumer and user acceptance of circular offerings is one of the primary barriers for the transition to a circular economy. Available studies on this topic have used data from hypothetical scenarions, reducing their explanatory potential. Today, established circular businesses such as fashion subscription services open new research opportunities to address such limitation. In this context, this paper assesses the suitability of user-generated online reviews as a novel source of information for investigating factors and conditions of acceptance and adoption of circular offerings in the fashion sector. Based on the data analysis, it was found that user-generated online reviews were credible, contributed with new insights regarding factors and conditions of acceptance and allowed us to analyse a significant number of observations. The reviews provided significant insights into economic factors and the impacts this type of offering had on the daily life of users, an aspect that has been studied rarely, so far. Nevertheless, this type of data sources also presented some limitations. On the one hand, and because of their nature, they did not offer significant insights into other consumer and user acceptance factors identified in the literature such as demographic, psycho-social and cultural factors. On the other hand, they only provided a snapshot of the user experience in a given moment of time. To address these limitations, we suggest future research on user and consumer acceptance of circular offerings should investigate ways to integrate user-generated online reviews with more traditional tools and methods to gain insigths into cultural and psychosocial factors, explore additional sources that can help capture the dynamics of service use such as social media, online communities and customer service chats, and expand data collection to other market segments.

Tourists’ shifting perceptions of UNESCO heritage sites: lessons from Jeju Island-South Korea

Purpose The purpose of this research is to analyze the shifting perceptions of international tourists to Jeju Island and provide practical lessons to the tourism industry. Specifically, in regard to three United Nations Educational, Scientific and Cultural Organization (UNESCO) natural World Heritage sites in Jeju, this research measures the most salient topics mentioned by tourists to inform a more accurate perception of the island’s most valuable natural assets as reported by tourism experiences. Design/methodology/approach This study used a Web crawler to gather over 1,500 English language reviews from international tourists from a famous travel information website. The collected data were then preprocessed for stemming and lemmatization. After this, the processed text data were analyzed through a latent Dirichlet allocation (LDA)-based topic modeling approach to identify the most prominent clusters of ideas mentioned and represent them visually through graphs, tables and charts. Findings The findings from this research suggest that there are ten identifiable topics. Topics focusing on “adventure,” “summits” and “winter” showed noticeable increases, whereas topics focusing on “sunrise peak” and “UNESCO” have decreased over time. There is a trend for international tourists to be ever more conscious of the adventurous and rugged aspects of Jeju, and the novelty of mentioning UNESCO status seems to have worn off. Furthermore, there is the proclivity for tourists to mention “worth” and “enjoy” more as time goes on. Originality/value This study applies LDA-based topic modeling and LDAvis using user-generated online reviews with time-series analyses. Consequently, it provides unique insights into the changing perceptions of ecotourism on Jeju today, as well as contribution to smart tourism fields.

Using Online User-Generated Reviews to Predict Offline Box-Office Sales and Online DVD Store Sales in the O2O Era

With the rapid growth of e-commerce and social media, customers post online reviews on various online shopping websites and social media after their consumption experience, which generated the electronic word of mouth effect. In the online-to-offline era, companies are using multi-channels to increase customer demand, and the Effect generated by online users’ reviews plays an important role in customer demand both online and offline. Few previous studies focused on using online user-generated reviews to forecast the demand of hyper-differentiated products online and offline, which is particularly hard to predict due to the various preferences of customers and complex relationship between factors. Via predictive global sensitivity analysis, this study uses online user-generated reviews posted on social media to predict customers’ demand for hyper-differentiated products both online and offline, with an example in the film industry. We generate forecasting equations, which can successfully predict movie box office and online DVD store sales. The effectiveness and reliability of our approach are proved by the numerical studies of 22 randomly selected movies. Managers can use our method to access and analyze online users’ review data and forecast future online and offline product sales.

Curvilinear effects of e-loyalty in China’s online tourism industry

PurposeThe purpose of this paper is to understand e-loyalty in the travel industry. Specifically, this paper aims to examine the curvilinear relationship between predictors and e-loyalty.Design/methodology/approachAn empirical study was conducted using an online survey with one of the largest travel companies in China. Structural equation modeling was used to test the models, and pair-wise nested F-tests were used to compare the models.FindingsResults show that the curvilinear model has greater explanatory power of loyalty than traditional linear models. The results of pair-wise nested F-tests show that the loyalty model exhibits statistically significant R2 improvement compared to the linear model. However, the R2 improvement in the integrated model is not statistically different from that in the linear model. Confirmation and satisfaction are found to be salient factors influencing loyalty.Research limitations/implicationsThis study makes important contributions to the online community literature by understanding the drivers of loyalty in the travel industry. However, there are limitations. First, this study addressed member loyalty of an online travel community with data collected from one company. Thus, generalizability is limited. Online communities and firms may have different characteristics, resulting in different factors influencing consumer loyalty. The authors plan in the future to collect data from other online travel companies and examine their model with different samples so as to check the generalizability of the current findings. Second, the authors collected a snapshot view on loyalty. Both researchers and managers note that small changes in loyalty and retention can yield disproportionately large changes in profitability (Reichheld et al., 2000). Consumer loyalty may change over time, so to maintain and increase profits, it is important to monitor such change. In the future, the authors plan to conduct a longitudinal study of community members to evaluate their loyalty over time.Practical implicationsAs China seeks to gain additional market share in the global tourism market, travel companies should make use of websites as a marketing tool to attract and retain customers. These actions enable a travel company to enhance its competitiveness. More and more people use the internet for tour deals, bookings and finding tour-related information. Effective use of websites can affect the competitiveness of ecommerce companies. E-vendors could assess and adopt the dimensions recommended in this paper to help better understand areas for improvement. It is common today for consumers to buy travel products online instead of going through a travel agent. Considering the importance of reciprocity in formulating consumer satisfaction and loyalty in the virtual environment, companies should monitor reciprocal behavior on the virtual community. With advancement in technologies, consumer behaviors have changed and more consumers prefer social interactions in the virtual world. Companies can analyze posts in the virtual environment to assess reciprocity and may design a mechanism to foster reciprocal behaviors. By leveraging reciprocity, firms can better connect satisfaction with loyalty. More than 70 per cent of executives surveyed by McKinsey (2012) said that they regularly generate value through their Web communities. In addition, to pay attention to consumer to consumer reciprocity in the virtual world, companies should listen to what customers say in their online community, as this attention is an indication of reciprocity between consumers and companies. The ideas and opinions expressed in the online community tell the company customers’ perception of the value of its products and customers’ needs. Such attention to the voices in the online community will help companies to better tailor products/services to meet customers’ needs. Furthermore, the voices expressed in the virtual community are also effective in developing and maintaining new internet marketing opportunities such as email marketing, giveaways, search engine optimization, pay per click and shopping comparison marketing. Companies interested in retaining and attracting customers should leverage their established virtual communities and pay close attention to online posts and evaluate members’ satisfaction. Such effort will provide tangible benefits. As shown in Ye et al.’s study, traveler reviews produce a significant impact on online sales (Ye et al., 2011), with a 10 per cent increase in traveler review ratings, boosting online bookings by more than 5 per cent. This finding suggests that businesses should link online user-generated reviews to business performance in tourism. Finding incentives for users to share might be one way to improve interactivity and further create stickiness on the part of the website.Originality/valueThis paper is one of the first studies to address the need to move beyond linear models of e-loyalty and to additionally examine potential curvilinear and interactive effects. This study also identifies key variables such as reciprocity and satisfaction as determinants of e-loyalty in the Chinese online travel and tourism industry.