Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach

Given the importance of online reviews, as shown by extensive research, we address the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling and social influence theories. We use review order and time interval to incorporate the moderating effects of the time-related variable on the reviewer’s valuation of products and the related details they provide. Applying deep learning techniques in text processing and model building on a dataset of 239297 reviews, the empirical findings represent strong support of the proposed approach and show its superior performance in predicting review helpfulness compared to current approaches. This research contributes to theory by analyzing online reviews from the points of two well-known information processing theories and contributes to practice by developing a model to sort the newly posted reviews

Profiling users' behavior, and identifying important features of review 'helpfulness'

The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people’s attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers

A Combinatorial Approach for Predicting Online Review Helpfulness of Indian Online Travel Agencies

Online user reviews are quite popular in social media, e-commerce and review websites. It is commonly referred as word of mouth which provides positive and negative messages from users about products and services. It helps users to get insights through review ratings and subjective feedback. As the volumes of reviews are high, it makes it harder for users to identify the helpfulness upfront. In general helpfulness rating is provided by the users who read the review, but many reviews still stay unrated. In this paper, we propose an approach of predicting helpfulness of such reviews from mouthshut.com using a combinatorial approach of empirical analysis and naïve Bayes machine learning method. The data set is chosen for Indian Online Travel Agencies (OTA) namely Makemytrip, Cleartrip, Yatra, Goibibo, and Expedia India. A detailed experiment is conducted and results are discussed by analyzing review metadata characteristics

Data Mining for Shopping Centres – Customer Knowledge-Management Framework

Shopping centers are an important part of the UK economy and have been the subject of considerable research. Relying on complex interdependencies between shoppers, retailers and owners, shopping centers are ideal for knowledge management study. Nevertheless, although retailers have been in the forefront of data mining, little has been written on Customer Knowledge Management for shopping centers. In this chapter, the authors aim to demonstrate the possibilities and draw attention to the possible implications of improving customer satisfaction. Aspects of customer knowledge management for shopping centers are considered using analogies drawn from an exploratory questionnaire survey. The objectives of a Customer Knowledge Management system could include increasing rental incomes and bringing new life back into shopping centers and towns

Identifying Features and Predicting Consumer Helpfulness of Product Reviews

Major corporations utilize data from online platforms to make user product or service recommendations. Companies like Netflix, Amazon, Yelp, and Spotify rely on purchasing trends, user reviews, and helpfulness votes to make content recommendations. This strategy can increase user engagement on a company\u27s platform. However, misleading and/or spam reviews significantly hinder the success of these recommendation strategies. The rise of social media has made it increasingly difficult to distinguish between authentic content and advertising, leading to a burst of deceptive reviews across the marketplace. The helpfulness of the review is subjective to a voting system. As such, this study aims to predict product reviews that are helpful and enable strategies to moderate a user review post to improve the helpfulness quality of a review. The prediction of review helpfulness will utilize NLP methods against Amazon product review data. Multiple machine learning principles of different complexities will be implemented in this review to compare the results and ease of implementation (e.g., Naïve Bayes and BERT) to predict a product review\u27s helpfulness. The result of this study concludes that review helpfulness can be effectively predicted through the deployment of model features. The removal of duplicate reviews, the imputing of review helpfulness based on word count, and the inclusion of lexical elements are recommended to be included in review analysis. The results of this research indicate that the deployment of these features results in a high F1-Score of 0.83 for predicting helpful Amazon product reviews

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

Do Customers Perceive Reviews as Manipulated? A Warranting Theory Perspective

Online customer reviews proved to have an influence on customer’s purchase. However, most online reviews don’t always prove effective in guiding the purchase process, because of fake reviews. While e-commerce platforms do tend to incorporate ways to counter review manipulation, customer perception on review quality is more important. In this study we aim to understand the impression mechanism of online reviews. Using warranting theory, as theoretical lens we found that textual and review characteristics play a crucial role in forming an impression amongst the customers. Further, research suggest that higher contamination of reviews influence customers to perceive reviews less authentic