Ranking online consumer reviews

YesProduct reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.Ministry of Electronics and Information Technology (MeitY), Government of India for financial support during research work through “Visvesvaraya PhD Scheme for Electronics and IT”

Design a Product Aspect Ranking Framework and Its Applications

Today lots of consumer reviews about products are present on the Internet. Consumer reviews reflect important knowledge about product that will be helpful for firms as well as users. The reviews are most of times not organized properly that going to difficulties in information and knowledge gaining. We proposes a product aspect ranking framework, that automatically determines the important aspects of products by using online consumer reviews, improving the usability of the frequent given reviews. The important aspects about product are determined depends on two observations: 1) the important aspects are often comment by numerous consumers 2) consumer opinions on the important aspects largely affect their overall opinions on the product. With the help of given consumer reviews of a product, we firstly identify aspects of product by shallow dependency parser and identify consumer opinions on these aspects by a sentiment classifier. After that developing a probabilistic aspect ranking to grab the importance of aspects by concurrently considering aspect frequency and the impact of consumer opinions given to every aspect over their allover opinions. We apply this ranking framework to two real-world applications, i.e., document-level sentiment classification and extractive review collection, that show significant performance improvements, that leads in giving the strength of product aspect ranking in promoting real-world applications

Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory

Preference change, also known as preference drift, is one of the factors that online retailers need to consider to accurately collect consumer preferences and make personalised recommendations. Online reviews have been widely used to analyse the preference drift of consumers. However, previous studies on online reviews ignored the psychological perceptions of consumers in terms of satisfaction. This paper aims to develop a method for dynamic preference elicitation from online reviews based on exploring the theory of consumer satisfaction formation. Based on the framework of expectation confirmation theory, we develop formulas for expressing the relations among expectation, perceived performance, confirmation, and satisfaction. We then use the proposed dynamic preference elicitation model to predict the change of consumer overall preference after each review and rank products for consumers’ next purchase. We test the proposed approach with a case study based on a data set from Amazon.com. It is founded that the satisfaction changes in each purchase, and this change will affect the prediction of the next product ranking. The case study is based on one product group, and further research is needed to see if the operation of the proposed method can be extended to other kinds of product

What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score

Online consumer reviews (OCRs) have become one of the most helpful and influential information in consumers purchase decisions. However, the proliferation of OCRs has made it difficult for consumers to orientate themselves with the wealth of reviews available. Therefore, it is paramount for online organizations to understand the determinants of perceived information diagnosticity in OCRs. In this study, we investigate consumer perceptions and we adopt the Elaboration Likelihood Model to analyze the influence of central (long, relevant, current, and factual OCRs) and peripheral cues (source credibility, overall ranking scores) on perceived information diagnosticity (PID). We consider the potential moderating effect of consumer involvement, and tested the robustness of the theoretical framework across time. Based on two surveys carried out in 2011 and in 2016, this study demonstrates the dynamic nature of the antecedents of PID in e-WOM. We found that long reviews are not perceived as helpful, while relevant and current reviews as well as overall ranking scores are perceived as diagnostic information in both samples. The significance of the predicting power of review factuality and source credibility has evolved over time. Both central (review quality dimensions) and peripheral cues (ranking score) were found to influence PID in high-involvement decisions

Unbalanced power relationship in digital markets between platforms and their complementors: can consumers come to the rescue?

Acknowledging the unbalanced power relationship between online platforms and their complementors, the economic dependence relationship and fear of retaliation may prevent complementors from fighting against economically harmful practices implemented by online dominant platforms. The economic dependence relationship and fear of retaliation are illustrated by past antitrust cases on both sides of the Atlantic. Having set the scene in which complementors might be disincentivised to take up legal actions facing anticompetitive practices, this paper takes the example of two distortions of information practices, implemented by dominant online platforms, that are harmful to both consumers and complementors: dark patterns and ranking biased by fake reviews. Under the angle of consumer empowerment (through direct complaints) and consumer-oriented enforcement (relying on competition law, the UCPD, and the Digital Services Act Package), this paper shows that consumer empowerment and consumer-oriented enforcement of distortion of information practices can produce a positive externality for complementors. Sole claims for damages have the lowest probability of producing a positive externality unless they act as a signal against an obligation non-implemented by an online platform. Injunctions and commitments have the highest probability of producing a positive externality for complementors. However, one of the constraints of this proposal may be the limited detectability of these practices by consumers

Credibility Analysis of Customer Reviews on Amazon: A Design Science Approach

This research examines the problem of identification and elimination of malicious customer reviews on Amazon.com. Online customer reviews are increasingly considered crowd-sourced consumer opinions that significantly influence online purchasing decisions (Hu, 2012). However, most current approaches to detecting fake reviews rely on either manual assessment of the reviews or the use of the mechanical Amazon Turks service (Mukherjee, 2014; Munzel, 2015). Manual assessment of customer reviews is not scalable in practice, leaving the quality of the current approaches to detect fake reviews questionable. The primary goal of our research is to develop a model of credibility analysis that automatically classifies amazon customer reviews as credible or non-credible. This model is developed based on the Design Science Research Methodology (Peffers, 2007) and encompasses a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) as a classification technique. We first identify features of online customer reviews that can be used to effectively separate credible reviews from non-credible ones. Then fed the review dataset based on identified features to our proposed model for the assessment of review’s credibility. The study of existing literature indicates that most of current research on fake review focusses on the content of the reviews (Hu et al., 2012; Munzel, 2015).We, however, believe that content is only part of an effective method for detecting fake reviews. Our proposed model considers not only the textual but also the writing style and user related features of reviews. Further, we will compare our LSTM based model with other algorithms used in detecting misleading information such as Dynamic Series-Time Structure-based Support Vector machine (SVM-DSTS) (Ma, Gao, 2015) and Decision tree ranking (DT-Rank) (Zhao, 2015). Initial Design of proposed model will be presented for this TREO talk and encourage discussion concerning misleading customer reviews, existing fake review elimination initiatives, and Design science as an approach

Please, talk about it! When hotel popularity boosts preferences

Many consumers post on-line reviews, affecting the average evaluation of products and services. Yet, little is known about the importance of the number of reviews for consumer decision making. We conducted an on-line experiment (n= 168) to assess the joint impact of the average evaluation, a measure of quality, and the number of reviews, a measure of popularity, on hotel preference. The results show that consumers' preference increases with the number of reviews, independently of the average evaluation being high or low. This is not what one would expect from an informational point of view, and review websites fail to take this pattern into account. This novel result is mediated by demographics: young people, and in particular young males, are less affected by popularity, relying more on quality. We suggest the adoption of appropriate ranking mechanisms to fit consumer preferences. © 2014 Elsevier Ltd

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Average Scores Integration in Official Star Rating Scheme

Purpose: Evidence suggests that electronic word-of-mouth (eWOM) plays a highly influential role in decision-making when booking hotel rooms. The number of online sources where consumers can obtain information on hotel ratings provided has grown exponentially. Hence, a number of companies have developed average scores to summarize this information and to make it more easily available to consumers. Furthermore, official star rating schemes are starting to provide these commercially developed average scores to complement the information their schemes offer. The purpose of this paper is to examine the robustness of these systems. Design/methodology/approach: Average scores from different systems, and the scores provided by one rating site were collected for 200 hotels and compared. Findings: Findings suggested important differences in the ratings and assigned descriptive word across websites. Research limitations/implications: The results imply that the application of average scores by official organizations is not legitimate and identifies a research gap in the area of consumer and star rating standardization. Originality/value: The paper is of value to the industry and academia related to the examination of rating scales adopted by major online review tourism providers. Evidence of malpractice has been identified and the adoption of this type of scales by official star rating schemes is questioned.Peer reviewe