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The purpose of this paper is to present an analysis of Goodreads’ user-generated book reviews from a linguistic perspective for insights into the psychological aspects of reviewers’ perceptions and behaviors. This examination of users’ language and perspectives may shed light on the role and value of user-generated reviews in complementing the traditional representation of resources and facilitating the discoverability of cultural objects.

This study involved a textual analysis of 474,803 unique reviews of Goodreads’ 2015 top-rated books generated by 9,335 Goodreads’ reviewers. In order to better understand the nuances of user-generated reviews, a content analysis was applied to 2,500 reviews of each of the five top-ranked titles in Goodreads’ Fiction Literature genre category.

The analysis of user-generated reviews demonstrates that language is a quite stable and reliable dimension across Goodreads’ users. The high rate of function words utilized, in particular I-words, coupled with positive emotion words, suggests that reviewers tended to convey their opinions in order to influence other individuals’ reading choices, or in Bourdieu’s (1985) terms, influence cultural production. In line with previous studies of user-generated reviews, the prevalence of positive reviews may also imply their unreliable nature. This study supports the importance of transparency regarding inclusion of user-generated reviews in traditional systems of knowledge representation, organization and discovery, such as WorldCat.

This study contributes to better understanding of linguistic characteristics of Goodreads’ reviews, including the role and value of user-generated reviews in complementing traditional representation of resources and facilitating discoverability of cultural objects.

In this study, STEM resources reviewed in Goodreads were investigated to determine their authorship, linguistic characteristics and impact. The analysis reveals gender disparity favoring titles with male authors.

This paper applies theoretical concepts of knowledge commons to understand how individuals leverage the affordances of the Goodreads platform to share their perceptions of STEM-related books.

The analysis reveals gender disparity favoring titles with male authors. Female-authored STEM publications represent popular science nonfiction and juvenile genres. Analysis of the scholarly impact of the reviewed titles revealed that Google Scholar provides broader and more diverse coverage than Web of Science. Linguistic analysis of the reviews revealed the relatively low aesthetic disposition of reviewers with an emphasis on embodied experiences that emerged from the reading.

This study contributes to the understanding of the impact of popular STEM resources as well as the influence of the language of user-generated reviews on production, consumption and discoverability of STEM titles.

The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences.

The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre.

The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre.

The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research.

Today, individuals use social media to express their opinions and feelings, which offers a living laboratory to researchers in various fields, such as management, innovation, technology development, environment and marketing. It is therefore necessary to understand how the language used in user-generated content and the emotions conveyed by the content affect responses from other social media users.

In this study, almost 700,000 posts from Twitter (as well as Facebook, Instagram and forums in the appendix) are used to test a conceptual model grounded in signaling theory to explain how the language of user-generated content on social media influences how other users respond to that communication.

Extending developments in linguistics, this study shows that users react negatively to content that uses self-inclusive language. This study also shows how emotional content characteristics moderate this relationship. The additional information provided indicates that while most of the findings are replicated, some results differ across social media platforms, which deserves users' attention.

This article extends research on Internet behavior and social media use by providing insights into how the relationship between self-inclusive language and emotions affects user responses to user-generated content. Furthermore, this study provides actionable guidance for researchers interested in capturing phenomena through the social media landscape.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

Online consumer reviews (OCRs) have emerged as a particularly important type of user-generated information about a brand because of their widespread adoption and influence on consumer decision-making. Much of the existing OCR research focuses on quantifiable OCR features such as star ratings and volume. More research that examines the influence of review elements, aside from numeric ratings, such as the verbatim text, particularly in services contexts is needed. The purpose of this research is to investigate the impact of service failures on consumer arousal and emotions.

The authors present three behavioral experiments that manipulate service failure and linguistic elements of OCRs by using galvanic skin response, survey measures and automated facial expression analysis.

Negative OCRs lead to the greatest levels of arousal when consumers read OCRs. Service failure severity impacts anger, and referential cohesion, an observable property of text that helps a reader better understand ideas in the text, negatively moderates the relationship between service failure severity and anger.

The authors are among the first to empirically test the effect of emotional contagion in a user-generated content context, demonstrating that it can occur when consumers read such content, even if they did not experience the events being described. The research uses a self-report and physiological measures to assess consumer perceptions, arousal and emotions related to service failures, increasing the robustness of the literature. These findings contribute to the marketing literature on OCRs in service failures, physiological measures of consumers’ emotions, the negativity bias and emotional contagion in a user-generated content context.

This study aims to explore the quality of user-generated content regarding readability, polarity, word length and diversity, as well as its implications for guest satisfaction in Las Vegas luxury gaming resorts.

This study examined 12,940 textual customer reviews from six luxury hotels in luxury gaming destination resorts via Google Travel gathered from SCTM 3 (Smart Crawling and Text Mining). Moreover, the regression analysis identified the relationship between the variables in the textual customer reviews and the customer’s overall satisfaction.

A key finding of this study revealed that word length moderates the relationship between readability and overall customer satisfaction negatively, whereas it positively moderates the path from sentiment polarity and diversity to overall customer satisfaction.

This study contributes to the relationship between technical aspects of online reviews. The adopted methodology allows us to precisely identify the essential attributes that influence customer satisfaction through textual reviews. Further, the study explores the quality of user-generated content, addressing aspects such as readability, polarity, diversity and word length, providing a unique perspective on how these specific elements directly impact customer satisfaction in this context of hotels in luxury in Las Vegas.

本研究探讨了用户生成内容的可读性、情感倾向、词长和多样性等方面的质量, 以及这些因素对拉斯维加斯豪华博彩度假村顾客满意度的影响。

本研究通过 SCTM 3（智能爬虫与文本挖掘）收集了谷歌旅行上的六家豪华酒店的12,940 条客户评论文本。此外, 回归分析确定了文本客户评论中的变量与客户整体满意度之间的关系。

本研究的一个关键发现是, 词长在可读性与整体顾客满意度之间的关系中起到负面调节作用, 而在情感倾向和多样性与整体顾客满意度之间的路径中起到正面调节作用。

本研究对在线评论的技术方面之间的关系作出了贡献。采用的方法使我们能够精确识别通过文本评论影响顾客满意度的关键属性。此外, 本研究探讨了用户生成内容的质量, 涉及可读性、情感倾向、多样性和词长等方面, 提供了独特视角, 揭示了这些具体元素如何直接影响拉斯维加斯豪华酒店顾客满意度。

The chapter proposes to investigate online reputation of hospitality brands and its measurements. Brand reputation is generally defined as an overall appraisal of a company by its stakeholders, which is the result of the company's past actions and predictions about the company's future (Ferguson, Deephouse, & Ferguson, 2000). Being viewed as the opinion shared among a group of stakeholders (Dowling, 2008), it plays an important role in the tourism industry. With the progress of Information Communication Technologies (ICTs), reviews and user-generated contents of destinations and of hospitality companies together with the related emerging brand reputation can influence consumers' behaviors and choices. Brand reputation analysis could be more useful in the hospitality brand management when integrated with brand image and brand identity analysis, mainly because in tourism businesses and destinations, brands are typically affected by an inherent fragility determined by the service nature of products (Casarin, 1996). According to Biel (1991), the meanings that consumers assign to a brand are synthesized into brand associations formed by the components perceived to underlie the brand's image. As well as brand reputation, strong, positive and unique associations reinforce a brand and increase its equity that requires significant internal brand identity efforts, which should create a corresponding brand image through integration in overall marketing programmes (Keller, 2003). It makes sense to develop an analytical research approach that compares online brand reputation (OBR) with brand association matching as a measure of the alignment between brand identity and brand image in hospitality companies. This comparative analysis emerging from brand reputation, brand image and brand identity analysis can reveal divergent situations (i.e., high brand reputation and low brand association matching) and orient brand managers in reviewing online brand communication. Brand reputation and brand image analysis will be contextualized in an online community as a social setting that is considered to be a new type of market (Muniz & O'Guinn, 2001). We focus on hospitality online communities populated by consumers and other actors such as influencers and bloggers: their brand perception could be separately compared with brand identity that we will extract from company communications including presentational information and brand-related press releases found on websites, nonfinancial narrative from annual reports, and interviews with managers published in mainstream media sources. In our analysis we will focalize on a cluster of luxury hospitality companies integrating a netnographic and text-mining techniques. We will use both the techniques in order to (1) extract and study brand associations in terms of brand reputation, brand image, and brand identity; (2) develop indicators of brand reputation and brand association matching; and (3) discuss their utility in the management of the hospitality company brands.

We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.