Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

Situmeang, Frederik; Boer, Nelleke de; Zhang, Austin

doi:10.1177/1470785319863619

Cited by 21 publications

(15 citation statements)

References 53 publications

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“…Neoteric methods have been applied to understand actual behaviors in BCs in the online context. The neoteric methods facilitate capturing the heartfelt and real‐time consumer discussions in social media by text‐mining techniques (Herhausen et al., 2019) and sentiment analysis to capture the consumer emotions behind the usage of certain words in social media (Situmeang et al., 2019). Finally, to expand the body of knowledge on BC research, we have offered new directions for future research.…”

Section: Discussionmentioning

confidence: 99%

Brand communities: A literature review and future research agendas using TCCM approach

Bhattacharjee

Pradhan

Swani

2021

Int J Consumer Studies

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Academic interest in brand community research has burgeoned in the past two decades. Despite its importance, there has been a paucity of effort in reviewing the growing research. Drawing on the Theory‐Context‐Characteristics‐Methods (TCCM) review framework, this research provides a comprehensive understanding of the dominant theories, contexts (i.e., industries and countries), characteristics (i.e., key variables and their relationships), and methods (i.e., research approaches and analysis techniques) employed in brand community research over last two decades (2001–2020). A systematic analysis of 285 articles reveals a focus on consumer‐specific theories and a scant application of multi‐disciplinary theories. Though the literature covers an array of industries to understand brand communities, there is a need for more research in the B2B context and the service industries. Furthermore, we detect a need for more research on the influence of brand/marketer‐related variables on brand communities, underlying mechanisms and boundary conditions for the key relationships in this area. This research presents four different stages of the evolution of brand community research over the years. Finally, we apply the TCCM framework to suggest fertile areas for future research.

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Section: Discussionmentioning

confidence: 99%

Brand communities: A literature review and future research agendas using TCCM approach

Bhattacharjee

Pradhan

Swani

2021

Int J Consumer Studies

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“…We use a multi-method approach, using the insight advantages of qualitative studies and the benefits that mining social commerce sites like Amazon represent for WOM communication (Humphreys and Wang, 2018; Kirmani and Campbell, 2004). We first use an automated text analysis method, sometimes used by researchers to make discoveries, find patterns in positive vs negative consumer reviews and evaluate differences between expert and consumer discourse in product-related comments (Lee and Bradlow, 2011; Netzer et al , 2012; Situmeang et al , 2020). This method also allows us to measure differences in language among groups and types of reviews (Humphreys and Wang, 2018).…”

Section: Methodsmentioning

confidence: 99%

Innocent until proven guilty: suspicion of deception in online reviews

Petrescu

Kitchen²,

Dobre

et al. 2022

EJM

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Purpose This study aims to formulate a new framework for identifying deception in consumer reviews through the lens of interpersonal deception theory (IDT) and the persuasion knowledge model (PKM). It evaluates variables contributing to consumer intentions to purchase after reading deceptive reviews and proposes deception identification cues to be incorporated into the interpersonal communication theoretical framework. Design/methodology/approach The first study is qualitative and quantitative, based on sentiment and lexical analysis of 1,000 consumer reviews. The second study uses the US national consumer survey with a partial least squares partial least squares-structural equation modeling and a process-based mediation–moderation analysis. Findings This study shows deceptive characteristics that cannot be dissimulated by reviewing consumers that represent review legitimacy based on review valence, authenticity, formalism and analytical writing. The results also support the central role of consumer suspicion of an ulterior motive, with a direct and mediation effect regarding consumer emotions and intentions, including brand trust and purchase intentions. Research limitations/implications This paper presents a new framework for identifying deception in consumer reviews based on IDT and PKM, adding new theoretical elements that help adapt these theories to written digital communication specificities. This study clarifies the role of suspicion in a deceptive communication context and shows the variables contributing to consumers’ purchase intention after reading deceptive reviews. The results also emphasize the benefits of lexical analysis in identifying deceptive characteristics of reviews. Practical implications Companies can consider the vulnerability of certain generations based on lower levels of suspicions and different linguistic cues to detect deception in reviews. Long-term, marketers can also implement deception identification practices as potential new business models and opportunities. Social implications Policymakers and regulators need to consider critical deception cues and the differences in suspicion levels among segments of consumers in the formulation of preventative and deception management measures. Originality/value This study contributes to the literature by formulating a new framework for identifying deception in consumer reviews, adapted to the characteristics of written digital communication. This study emphasizes deception cues in electronic word-of-mouth and provides additional opportunities for theorizing deception in electronic communication.

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“… Tao & Kim (2019) used OCRs to find a new attribute of customer satisfaction which is onshore cruiser experiences attributes. Situmeang, de Boer & Zhang (2020) comprehended customer satisfaction using OCRs and affirmed OCRs can develop a sustainable strategy for the restaurant industry. Based on the above findings, this study utilizes OCRs to discover the vital attributes of OTA customer satisfaction.…”

Section: Literature Reviewsmentioning

confidence: 99%

Determining the factors affecting customer satisfaction using an extraction-based feature selection approach

Riantama

2022

PeerJ Computer Science

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The coronavirus disease 2019 (COVID-19) causes tremendous damages to the world, including threats to human’s health and daily activities. Most industries have been affected by this pandemic, particularly the tourism industry. The online travel agencies (OTAs) have suffered from the global tourism market crisis by air travel lockdown in many countries. How online travel agencies can survive at stake and prepare for the post-COVID-19 future has emerged as an urgent issue. This study aims to examine the critical factors of customers’ satisfaction to OTAs during the COVID-19 pandemic. A text mining method for feature selection, namely LASSO, was used to deal with online customer reviews and to extract factors that shape customers’ satisfaction to OTAs. Results showed that refunds, promptness, easiness and assurance were ranked as the most competitive factors of customers’ satisfaction, followed by bad reviews & cheap and excellent service & comparison. New factors to customers’ satisfaction were revealed during the global tourism recession. Findings provide OTAs guidelines to reset services priorities during the pandemic crisis.

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Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

Cited by 21 publications

References 53 publications

Brand communities: A literature review and future research agendas using TCCM approach

Brand communities: A literature review and future research agendas using TCCM approach

Innocent until proven guilty: suspicion of deception in online reviews

Determining the factors affecting customer satisfaction using an extraction-based feature selection approach

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