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

Determinants of online review helpfulness that steer consumer purchase decision and their willingness to give review:an extended study in a cross-cultural context

Abstract. The increased use of social media and other online platforms have enabled consumers to communicate and discuss the products and services of brands with others. Consumers’ look for information in online reviews that assist them in informed purchase decisions. Previous literature has identified factors that influence consumers in adopting those online reviews, but whether consumers are willing to provide an online review after the purchase decision is not yet been studied previously. Another gap in the literate that is addressed is to base this study on output obtained from two countries. Therefore, our study is aimed at identifying factors that contribute to a consumer purchase decision and their willingness to give a review in a cross-cultural context. Our study aimed at restaurant reviews in Finland and Pakistan. Adopting and extending the Information Acceptance Model (IACM) proposed by Erkan and Evans (2016), that is developed by integrating Information Adoption Model (IAM) and related aspects of Theory of Reasoned Action (TRA). This study examines the influence of online review helpfulness factors on consumer purchase decision, consequently influencing them to give a review to others. We also aim to identify if review adoption directly influences consumers in providing online review without purchasing the product or service. The proposed model of our study was validated through Structural Equation Modelling by using Smart Partial Least Squares software. A questionnaire was adopted from earlier studies. The questionnaire was measured on a sample size of 104 from Finland and 141 from Pakistan. This study identified review adoption leading towards consumer purchase decision, whereas, onsumers’ willingness to give is not directly linked with their adoption of information, but it is a post-purchase process. The commonalities between the two countries depict the needs of information behind seeking online review information. If the required information is being provided to the customer through online reviews, it will lead to review adoption. Generally, review positiveness, review perceived informativeness and review quality were identified most important factors in consumers review adoption that leads consumers in choosing a restaurant and try the food there. Whereas, the general attitude of consumers towards online reviews was found to be the most exciting factors identified in Pakistan output. Consumers’ perception of online reviews encourages them to read online reviews, and they think that it is always a risk to try a restaurant without referring to online reviews. Pakistani consumers find online reviews useful, providing relevant information about the restaurants that help them in choosing the best restaurant

Antecedents of Online Customers Reviews’ Helpfulness: A Support Vector Machine Approach

Online customer reviews (OCRs) have become an important part of online customers’ decision making and People use online reviews to make decision to buy or not to buy products and services. This study aims to answer two research questions: (1) what are the antecedents of helpfulness of online reviews based on their contents? (2) How do content-based cues on OCRs influence their helpfulness? We posit a research model to study the effect of peripheral and central cues in OCRs on online review helpfulness. Online review web pages will be collected from Amazon website using a web crawler. This article will be one of the first studies that investigate OCRs helpfulness based on the central cues in the text of the review. In addition, this research will be the first study that applied the support vector machine as a machine learning method to analyze the text of OCRs

Understanding, Analyzing and Predicting Online User Behavior

abstract: Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.Dissertation/ThesisDoctoral Dissertation Business Administration 201

Essays on the Influence of Review and Reviewer Attributes on Online Review Helpfulness: Attribution Theory Perspective

With the emergence of digital technology and the increasing availability of information on the internet, customers rely heavily on online reviews to inform their purchasing decisions. However, not all online reviews are helpful, and the factors that contribute to their helpfulness are complex and multifaceted. This dissertation addresses this gap in the literature by examining the antecedents that determine online review helpfulness using attribution theory. The dissertation consists of three essays. The first essay examines the impact of authenticity (review attribute) on review helpfulness, showing that the expressive authenticity of a review enhances its helpfulness. The second essay investigates the relationship between the reviewer attributes i.e., motivation, activity, and goals in online reviews. The study employs various machine learning techniques to investigate the influence of these factors on reviewers\u27 goal attainment. The third essay explores how the reviewer attributes are related to the helpfulness of online reviews. The dissertation offers significant theoretical and practical implications. Theoretically, the dissertation provides new insights into novel review and reviewer attributes. The study proposes a taxonomy of online reviews using means-ends fusion theory offering a framework for understanding the relationships between different components of online reviewer attributes and their contribution to the attainment of specific goals, such as emotional satisfaction. The study also highlights the importance of understanding the motivations and activities of online reviewers in predicting emotional satisfaction and the conditional effects of complaining behavior on emotional satisfaction. The findings inform review platform owners, business owners, reviewers, and prospective consumers in decision-making through helpful reviews. To review platform owners, the findings help segregate helpful reviews from the humongous number of reviews by determining the authenticity of the review. To business owners, the findings can help in understanding consumer behavior and taking necessary actions to provide better service to their customers. To reviewers, this dissertation can act as a guideline to write helpful reviews and to determine their helpfulness. Finally, to consumers or review readers, this dissertation provides an understanding of helpful reviews, thus allowing them to take product or service purchase decisions

Hotel online reviews: creating a multi-source aggregated index

Purpose This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. Design/methodology/approach This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating. Findings Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment. Originality/value This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languagesinfo:eu-repo/semantics/acceptedVersio

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

Why are some reviews perceived as more helpful than others?

User-generated reviews (UGR) are valuable in online markets, but not all reviews impact prospective customers equally. Reviews rated more helpful are more persuasive and valuable than others. Literature has examined how consumers evaluate the helpfulness of online reviews. We examine and demonstrate that content and non-content cues are important to driving the helpfulness of online reviews and that these two cues are incongruently influential to perceived helpfulness regarding salience stimuli readers’ attention. Specifically, a high salience of content cues (acceptable long and concrete content) and a high salience of non-content cues draw readers\u27 attention, subsequently influencing the higher perceived helpfulness compared with the low and the high content and non-content cues, respectively. Our findings provided evidence that information cues stemming from attributes of UGR can compensate interchangeably with information cues retrieved from the content of UGR

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