Exploring Latent Semantic Factors to Find Useful Product Reviews

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews

How is the review helpfulness evaluated?

A user-generated review that is perceived as helpful is valuable for both customer and the retailer, and that is why online markets such as Amazon.com collect public opinion on reviews that are perceived more helpful. Review platforms allow customers to vote for reviews they deem helpful. While prior literature has examined what drives the helpfulness of reviews, many of these studies have looked at drivers of perceived helpfulness of reviews in isolation. Using the lens of dual process theory, this research examines how consumers evaluate the helpfulness of a review. We propose a framework and provide empirical evidence for the evaluation of the review helpfulness process. We find that extreme reviews have a higher effect on review helpfulness compared to moderate reviews, and this effect is mediated by the depth and sentiment of the review content

Towards building a review recommendation system that trains novices by leveraging the actions of experts

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find helpful information about a product (Porter, 2010). Thus, there is a need to train reviewers to write better reviews, which could in turn better serve customers, vendors, and online e-stores. In this Thesis, we propose a review recommendation framework to train reviewers to better write about their experiences with a product by leveraging the behaviors of expert reviewers who are good at writing helpful reviews. First, we use clustering to model reviewers into different classes that reflect different skill levels to write a quality review such as expert, novice, etc. Through temporal analysis of reviewer behavior, we have found that reviewers evolve over time, with their reviews becoming better or worse in quality and more or less in quantity. We also investigate how reviews are valued differently across different product categories. Through machine learning-based classification techniques, we have found that, for products associated with prevention consumption goal, longer reviews are perceived to be more helpful; and, for products associated with promotion consumption goal, positive reviews are more helpful than negative ones. In this Thesis, our proposed review recommendation framework is aimed to help a novice or conscientious reviewer become an expert reviewer. Our assumption is that a reviewer will reach the highest level of expertise by learning from the experiences of his or her closest experts who have a similar evolutionary pattern to that of the reviewer who is being trained. In order to provide assistance with intermediate steps for the reviewer to grow from his or her current state to the highest level of expertise, we want to recommend the positive actions—that are not too far out of reach of the reviewer—and discourage the negative actions—that are within reach of the reviewer—of the reviewer’s closest experts. Recommendations are personalized to fit the expertise level of reviewers, their evolution trend, and product category. Using the proposed review recommendation system framework we have found that for a random reviewer, at least 80% of the reviews posted by closest experts were of higher quality than that of the novice reviewer. This is verified in a dataset of 2.3 million reviewers, whose reviews cover products from nine different product categories such as Books, Electronics, Cellphones and accessories, Grocery and gourmet food, Office product, Health and personal care, Baby, Beauty, and Pet supplies. Advisor: Leen-Kiat So

A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews

© 2020 Edith Cowan University With the proliferation of user generated online reviews, uncovering helpful restaurant reviews is increasingly challenging for potential consumers. Heuristics (such as “Likes”) not only facilitate this process but also enhance the social impact of a review on an Online Opinion Platform. Based on Dual Process Theory and Social Impact Theory, this study explores which contextual and descriptive attributes of restaurant reviews influence the reviewee to accept a review as helpful and thus, “Like” the review. Utilising both qualitative and quantitative methodologies, a big data sample of 58,468 restaurant reviews on Zomato were analysed. Results revealed the informational factor of positive recommendation framing and the normative factors of strong argument quality and moderate recommendation ratings, influence the generation of a reviewee “Like”. This study highlights the important filtering function a heuristic can offer prospective customers which can also result in greater social impact for the Online Opinion Platform

Revisiting Review Depth in Search for Helpful Online Reviews

This study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms

Do Human Faces Matter? Evidence from User-Generated Photos in Online Reviews

The importance of online reviews in e-commerce cannot be overstated, but few studies have focused on user-generated photos (UGPs) in reviews, especially human faces in UGPs. In this study, using Amazon online review data, we divide online reviews into text with UGPs, UGPs with faces, and UGPs with multiple faces based on the presence and number of faces, and discuss their effects on review helpfulness. Drawing on media richness theory and emotional contagion effects, we argue that faces provide a richness of information that can increase the effectiveness of photos as information mediators. Moreover, we argue that facial expressions and emotional states, as read-in and read-out devices that convey individual emotions, affect other consumers\u27 perceived review helpfulness. This study contributes to the literature on online reviews, media richness theory, and emotional contagion effects, while providing practical insights for e-commerce sites and consumers seeking to write effective online reviews

A Meta-Analysis on the Determinants of Online Review Helpfulness

Online consumer reviews can help customers decrease uncertainty and risk faced in online shopping. However, information overload and conflicting comments in online reviews can get consumers confused. Therefore, it is important for both researchers and practitioners to understand the characteristics of helpful reviews. But studies examining the determinants of perceived review helpfulness produce mixed findings. We review extant research about the determinant factors of perceived helpfulness. Conflicting findings exist for six review related factors, namely review extremity, review readability, review total votes, linear review rating, quadratic review rating, and review sentiment. We conduct a meta-analysis to reconcile the contradictory findings on the influence of review related factors over perceived review helpfulness. The meta-analysis results confirm that review extremity, readability, total votes, and positive sentiment have a negative influence on helpfulness, but review rating is positively related to helpfulness. We also examine those studies whose findings are contradictive with the meta-analysis results. Measure discrepancy and reviewed product type are the two main reasons why mixed findings exist in extant research