Online Reviews Research Articles

Consumer Sentiment and Hotel Aspect Preferences Across Trip Modes and Purposes

Travelers’ perceptions of hotels and their aspects have been the focus of much research and are often studied by analyzing consumers’ online reviews. Yet, little attention has been given to the effect of the trip mode, i.e., whether the person travels alone or with others, on travelers’ preferences as sentiment. Here, we study the influence of the trip mode and purpose using a mixed-methods approach. We conducted a user study to evaluate the perceptions of reviews across trip modes and found that star ratings do not consistently capture the sentiment in text reviews; on average, solo travelers’ text reviews are perceived as more negative than the star ratings they assigned, whether they travel for business or pleasure. We then analyzed over 137,000 reviews from TripAdvisor and Venere and found that a co-occurrence network approach naturally divides the text of reviews into hotel aspects. We used this result to measure the importance of hotel aspects across various traveler modes and purposes and identified significant differences in their preferences. These findings underscore the need for personalized marketing and services, highlighting the role of trip mode in shaping online review sentiment and traveler satisfaction.

Evolutive multi-attribute decision making with online consumer reviews

In the digital age, the sheer volume of online consumer reviews imposes a cognitive burden on consumers, complicating their purchasing decisions. Many studies have integrated consumer opinions to provide consumers with clear and concise information. However, these studies often prioritize mainstream opinions, overlooking the diversity and timeliness of other important perspectives. To address this challenge, we propose an evolutive decision-making method. Firstly, we propose an attribute rating evolution algorithm to address the online reviews based on the iterative self-organizing data analysis technique and time decay. This algorithm enables real-time analysis of the diverse opinions expressed in review data. Then, taking into account consumer attribute preferences and decision-making psychology, we formulate multiple product ranking strategies to offer personalized decisions based on the evolutive opinions. Our method decreases the bias towards review quantity, ensuring that significant opinions are not overshadowed by more frequent ones. Through data experiments and an application on OpenTable.com, we demonstrate that our method can provides effective decision recommendation for consumers.

Fake Review Detection Model Based on Comment Content and Review Behavior

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants.

How dark traits and regulatory focus manifest in online hotel reviews: a textual content analysis

ABSTRACT This study seeks to explore how Dark Triad of psychopathy, Machiavellianism, and narcissism interact with consumers’ regulatory focus (prevention-focus and promotion-focus) to influence online consumer reviews in the hotel industry. We conducted an empirical analysis of 1,185,940 hotel reviews from TripAdvisor.com, utilizing the textual content of online reviews and employing Language Inquiry and Word Count analytical tools. The research reveals a multifaceted link between regulatory focus and Dark Triad in online reviews. The results showed that individuals with a promotion focus tend to give higher ratings than those with a prevention focus. Dark Triad significantly influences how regulatory focus impacts online reviews. This study initiates a new academic dialogue on the interaction between regulatory focus and Dark Triad traits within online consumer behavior studies. It provides practical guidance for the hotel industry to refine their management and response strategies by acknowledging the impact of consumer personality traits on online reviews.

Empowering Consumer Decision-Making: Decoding Incentive vs. Organic Reviews for Smarter Choices Through Advanced Textual Analysis

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in purchases. The data analysis reveals major factors such as costs, support, usability, and product features that may influence the impact. The analysis also highlights the indirect impact of company size, the direct impact of user experience, and the varying impacts of changing conditions over the years on the volume of incentive reviews. This study uses methodologies such as Sentence-BERT (SBERT), TF-IDF, spectral clustering, t-SNE, A/B testing, hypothesis testing, and bootstrap distribution to investigate how semantic variances in reviews could be used for personalized shopping experiences. It reveals that incentive reviews have minimal to no impact on purchasing decisions, which is consistent with the credibility and consistency analysis in terms of volume, length, and content. The negligible impact of incentive reviews on purchase decisions underscores the importance of authentic online feedback. This research clarifies how review characteristics sway consumer choices and provides strategic insights for businesses to enhance their review mechanisms and customer engagement.

A Kansei engineering-based decision-making method for offline medical service quality evaluation with multidimensional attributes

The evaluation of doctors’ offline medical service quality plays a vital role in evaluating hospital performance. There are two challenges that need to be addressed: (1) Currently, telephone follow-up and questionnaire surveys are the main methods for evaluating the quality of offline (mainly outpatient and inpatient) medical services in hospitals. However, these methods suffer from drawbacks such as low efficiency and resource consumption. (2) There are certain limitations to determining attribute dimensions and weighting patient evaluation attributes based solely on word frequency. To this end, this study proposes a Kansei engineering-based decision-making method for offline medical service quality evaluation with multidimensional attributes. First, this study constructs a medical Kansei lexicon based on speed and cosine similarity. Second, a concept of Kansei utility values is been defined to represent attribute values in medical service quality evaluation. Additionally, this study builds upon the Kano model to obtain the Kano classification of each attribute. On that basis, a novel approach to obtaining the implicit importance of the attributes is proposed. Thus, the weight vector of the attributes can be calculated as the combination of explicit and implicit importance. Finally, the evaluation result is obtained by using the multiattribute decision-making method. The effectiveness of the proposed method is verified through an analysis of online reviews from Haodf.com. The case study reveals that offline patients consider medical ethics and communication skills as the ”must-be” dimension of attributes, medical competence as a one-dimensional attribute, and medical advice and prescriptions as the attractive dimension of attributes. A comparative analysis with the traditional method is conducted to demonstrate the importance of dimensional analysis for offline medical service quality evaluation. The main contributions of this study include the following: (1) A medical Kansei lexicon is constructed based on Kansei engineering, and (2) a novel decision-making framework for medical service evaluation based on both appearance and implicit importance is constructed.

Online travel agency or blockchain-based platform?

Online travel agency has been used by most hotels to attract customers. However, hefty commissions and unverified reviews reduce hotels' cooperation intentions. Armed with unique advantages, blockchain-based platforms with higher blockchain-adoption costs can alternatively offer peer-to-peer booking service. This paper investigates the hotels' choice strategies on online travel platforms, blockchain-adoption conditions, and effects of blockchain-based platform. The results show that marginal blockchain cost, commission rate and customers' perceived distrust value of online reviews have great influence on the hotels' decisions. Although blockchain-based platforms can create win-win situations for hotel(s) and customers, they are not widely available as it is considered imperfect and costly. Alarmingly, there exists an invalidation effect of online travel platforms in a competitive market, resulting in non-equilibrium.

From text to insights: leveraging NLP to assess how landscape features shape tourist perceptions and emotions toward traditional villages

Abstract Understanding tourist perceptions and the relationship between landscape features and emotional attitudes in traditional village is crucial for sustainable development. However, quantifying these perceptions over vast spatiotemporal scales is challenging. Additionally, there is a paucity of knowledge on which landscape features influence tourist perceptions within varied samples on a spatial scale. In this study, we analyzed 39,130 online reviews of 57 traditional Chinese villages from 2018 to 2023. Utilizing Word2Vec and machine learning techniques, we identified 14 key landscape feature indicators. These were quantitatively scored using expert evaluations, and their relationship to tourists’ emotional attitudes was determined through linear regression analysis. The analysis revealed that architectural elements, service facilities, convenience, and sanitation are positively associated with tourists’ emotional attitudes towards traditional villages. Contrary to expectations, the frequency of landscape feature mentions did not correlate significantly with emotional attitudes, challenging traditional assumptions about the visibility of landscape features and their impact.

Content analysis of online reviews for dental clinics in Saudi Arabia

Content analysis of online reviews for dental clinics in Saudi Arabia

Survey on Fake Review Detection System

Abstract: In today's business landscape, online reviews play a crucial role in shaping commerce. A large portion of purchase decisions for online products is driven by customer feedback. Consequently, some individuals or groups try to manipulate product reviews to their advantage. Fake online reviews have a considerable effect on the experiences of online consumers, sellers, and e-commerce platforms. Although there has been academic research focused on identifying fake reviews, there is still a need for studies that thoroughly examine and summarize their origins and impacts. This work introduces a combination of semi-supervised and supervised text mining models to detect fake reviews, comparing their performance using datasets from reviews.

Attributes of satisfaction and dissatisfaction with online travel experiences in peer-to-peer platforms

While tourists have traditionally used peer-to-peer platforms for accommodation and dining, they can now also access travel experiences through these platforms thanks to a new product: online travel experiences. With the emergence of this product, a fundamental understanding of its key attributes and their impact on user satisfaction is essential. However, such understanding has been rarely explored. This research aims to address the knowledge gap by understanding Airbnb Online Experiences from the perspective of tourists' satisfaction. Utilizing the Kano model, this study identifies six key attributes of Airbnb Online Experiences and analyzes their dynamic effects on tourist satisfaction through aspect extraction and regression analysis of online reviews. This research highlights how the relationship between these attributes and tourist satisfaction evolved during the COVID-19 pandemic. This study serves as a reference for future research on online travel experiences by elucidating the components of this emerging product.

Exploring the mediating role of brand trust in the impact of digital marketing and online reviews on purchase decisions for beauty products: A case study on students in solo

The rapid development of information and communication technology has significantly altered the way consumers interact with brands. In the digital era, digital marketing has emerged as a primary strategy for companies to reach consumers and promote their products. The beauty industry, in particular, has undergone a major transformation, with digital marketing and online reviews playing crucial roles in influencing consumer purchasing decisions. This study focuses on exploring the mediating role of brand trust in the relationship between digital marketing, online reviews, and beauty product purchasing decisions among university students in Solo, Indonesia. The research aims to provide insights into how brand trust, as influenced by digital marketing and online reviews, impacts consumer behavior. The findings are expected to help beauty brands in enhancing their digital marketing strategies and improving brand trust among young consumers.

The Digital Voice of Students: Analysing Emerging Themes in a Corpus of University Reviews

For higher educational institutions, reviews on Google Maps offer a free and easily accessible platform for students to express their needs and voice their concerns voluntarily. Such a platform can be valuable for capturing a wide range of academic and non-academic aspects that may not be manifested via traditional feedback instruments such as surveys. Accordingly, this study explores the themes that emerge in a corpus of online reviews collected from Google Maps of 29 Saudi public universities while also highlighting how such reviews form a public discourse shaped by the medium itself. To this end, this research employed an NLP advanced technique, namely BERT, to categorise the reviews into themes based on their semantic similarity. Then, a thematic analysis was conducted to reveal six main recurrent themes in the corpus: location and accessibility, facilities and infrastructure, academic quality and teaching, student support services, religious sentiment and gratitude, and community and social environment. The findings indicate that while Google Maps reviews capture a number of cultural and social aspects of university life, they inherently foreground physical (e.g., amenities) and logistical (e.g., location) dimensions. The paper also demonstrates how online reviews provide students with a platform to raise issues related to both micro-level (e.g., specific courses, amenities) and macro-level concerns (e.g., inclusivity), hence their potential value in contributing to the evaluative discourse on higher education institutions. These results suggest the need for a systematic engagement with online feedback platforms to promote continuous institutional improvement and cultivate an inclusive, student-centred educational environment.

Fake Review Detection Using Machine learning and Deep Learning

The widespread adoption of Web 2.0 platforms has enabled consumers to share their opinions on products and services, influencing purchasing decisions. However, the proliferation of spam reviews has undermined the credibility of online reviews. This study aims to identify and evaluate existing approaches for detecting reviews, individual spammers, and Organizations. We categorized machine learning (ML) and deep learning (DL) techniques used for Review detection and assessed their effectiveness. Our findings indicate that accuracy is the most frequently used metric (25%), followed by recall (24%) and precision (22%). Additionally, we observed that utilizing the entire Amazon dataset can enhance the performance of F-measure, AUC, and F1-score metrics by 7%. Our study concludes that SMS spam filtering strategies are often effective in combating spam reviews. Furthermore, we developed a taxonomy of existing methodologies and observed a significant number of studies employing SMS anti-spam applications. This research uncovered innovative applications of ML and DL to spam review detection, offering a novel approach to addressing this issue. Our findings provide both academics and practitioners with a deeper understanding of the challenges in spam review identification and potential avenues for improvement using ML techniques.

AI-Powered Eye Tracking for Bias Detection in Online Course Reviews: A Udemy Case Study

The rapid growth of e-learning increased the use of digital reviews to influence consumer purchases. In a pioneering approach, we employed AI-powered eye tracking to evaluate the accuracy of predictions in forecasting purchasing patterns. This study examined customer perceptions of negative, positive, and neutral reviews by analysing emotional valence, review content, and perceived credibility. We measured ‘Attention’, ‘Engagement’, ‘Clarity’, ‘Cognitive Demand’, ‘Time Spent’, ‘Percentage Seen’, and ‘Focus’, focusing on differences across review categories to understand their effects on customers and the correlation between these metrics and navigation to other screen areas, indicating purchasing intent. Our goal was to assess the predictive power of online reviews on future buying behaviour. We selected Udemy courses, a platform with over 70 million learners. Predict (version 1.0.), developed by Stanford University, was used with the algorithm on the consumer neuroscience database (n = 180,000) from Tobii eye tracking (Tobii X2-30, Tobii Pro AB, Danderyd, Sweden). We utilised R programming, ANOVA, and t-tests for analysis. The study concludes that AI neuromarketing techniques in digital feedback analysis offer valuable insights for educators to tailor strategies based on review susceptibility, thereby sparking interest in the innovative possibilities of using AI technology in neuromarketing.

Dimensions of Job Demands Among New-Generation Employees Based on Online Reviews by Employees

Based on employees’ online reviews, this article analyzes the dimensions of job demand for new-generation employees, using a combination model of Latent Dirichlet Allocation, decision-making trials and evaluation laboratory, interpretative structural modeling, and cross-impact matrix multiplication (LDA—DEMATEL—ISM—MICMAC). The results show that job demand is composed of 10 dimensions, and there is significant interdependence between the dimensions. Changing one dimension will quickly affect the other dimensions. The dimension with the greatest influence degree is leadership and team atmosphere (s1), while the dimension with the highest affected degree and centrality degree is welfare and promotion (s3). Leadership and team atmosphere (s1), company culture and industry (s4), overall environment and platform (s5), and platform and technology (s7) were identified as the key factors. They play a causal role in job demand and have a significant impact on other dimensions. Dimensions such as working relationship and intensity (s2), welfare and promotion (s3), opportunity and resources (s6), business and industry development (s8), corporate prospects and personal development (s9), and work stress and position (s10) are affected by other dimensions and require special attention. The underlying need is present for the dimension of leadership and team atmosphere (s1), which has a direct or indirect impact on other dimensions in different ways.

Making platform recommendations more responsive to the expectations of different types of consumers: a recommendation method based on online reviews

Making platform recommendations more responsive to the expectations of different types of consumers: a recommendation method based on online reviews

On Translated Modern Iraqi Fiction in English: Target Readership Perspective

English translation of modern Iraqi fiction remains uncharted territory mainly from target readers’ perception. For this purpose, the paper aims to investigate the target ordinary readers’ perception of modern Iraqi novel translated into English using a collected corpus of reviews published in Goodreads online review platform. The study adopts a case study approach to examine target readers’ comments on Ahmed Saadawi’s Frankenstein in Baghdad (2013), translated by Jonathan Wright (2018). The novel has succeeded in reaping a number of international literary awards; it was also on the shortlist for the Man Booker International Prize in 2018. This paper draws on non-professional (ordinary) readers’ reviews to explore how English literary readers have welcomed, although slowly, the presence of contemporary Iraqi fiction. In line with this view of readership, a corpus of 164 text reviews and 2030 ratings were collected from Goodreads platform and analyzed to understand the ordinary English-speaking readership perception of the translated novel as to the features of a favorably received translation of Iraqi fiction. Therefore, the study has two aims to address: first, to identify the key features which may impede/enhance the non-professional readers’ enjoyment of the translated novel under study; second, to introduce a brief account of modern Iraqi translated fiction. The study is significant in that it helps to encourage future research on readers’ reception of modern Iraqi fiction translated into other languages and also to fill in a gap in literary translation studies on target ordinary readers’ perspective.

NetSpam : A Network-Based Spam Detection Framework for Reviews in Online Social Media.

The system ensures about irrelevant or promotional comments on blogs, forums, or social media. In today’s digital age, online reviews play a crucial role in shaping consumer decisions and business reputations. However, the open nature of these platforms also provides opportunities for malicious actors to post spam reviews, misleading users and undermining the credibility of genuine feedback. Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area.

Examining product quality and competitiveness via online reviews: An integrated approach of importance performance competitor analysis and Kano model

Understanding and enhancing product quality and competitiveness are vital in today's market. By leveraging the significant potential of online reviews for market-wide consumer research and strategic quality enhancement, this study introduces a novel review analytics approach that combines Importance Performance Competitor Analysis (IPCA) with the Kano model to evaluate and improve product attributes. The proposed Kano-Weighted IPCA (KWIPCA) framework assesses the importance and performance of product attributes by independently weighting positive and negative reviews according to the principles of the Kano model, while also considering multiple competitors in the market. The final output is a user-friendly, four-quadrant matrix that provides comprehensive insights into the market importance of attributes, their relative achievement, and competitors' performance distribution. This KWIPCA matrix enables the evaluation of a product's strengths and weaknesses relative to competitors, identification of critical areas needing immediate improvement, and offers strategic guidance for enhancing overall customer satisfaction. To demonstrate and validate the KWIPCA framework, a case study was conducted using 8567 reviews of 22 five-star hotels in Seoul, South Korea. The results confirm the KWIPCA's effectiveness in providing actionable insights and competitive benchmarking.