The Effects of Gender Signals and Performance in Online Product Reviews

This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed next to products. Differences in reviews, perceived—consciously or unconsciously—with respect to gender signals, can lead to crucial biases in determining what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names to select reviews where the author’s likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep learning classifier to quantify the gendered writing style (i.e., gendered performance) of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on the review helpfulness ratings using matching experiments. This is aimed at understanding if an advantage is to be gained by (not) signaling one’s gender when posting reviews. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, we believe this general chain of tools could be deployed across various social media platforms.

Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.

Determinants of online clothing review helpfulness: the roles of review concreteness, variance and valence

Online reviews have emerged as an essential information source for online clothing purchasing behaviour. It is thus paramount for marketers to understand what makes online clothing review helpful to consumers. This research primarily aims to examine the relationship between review textual content factors and review helpfulness in the context of online clothing purchasing. Experiments on review concreteness (concrete or abstract), review variance (consistent or inconsistent) and review valence (positive or negative), between participants were conducted to explore the interaction effect. The findings suggest that online clothing review concreteness, variance and valence are significant factors affecting review helpfulness. Additionally, this study’s findings show that abstract review, negatively review and inconsistent review has a stronger effect on online clothing review helpfulness than concrete review, positively review and consistent review. The findings will help customers to write better clothing reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.

The Challenges of Modeling and Predicting Online Review Helpfulness

Predicting review helpfulness is an important task in Natural Language Processing. It is useful for dealing with the huge amount of online reviews on varied domains and languages, helping and guiding users on what to read and consider in their daily decisions. However, there are limited initiatives to investigate the nature of this task and how hard it is. This paper aims to fulfill this gap, providing a better understanding of it. Two complementary experiments are performed in order to uncover patterns of usefulness evaluation as performed by humans and relevant features for machine prediction. To assure our results, we run the experiments for two different domains: movies and apps. We show that humans agree on the process of assigning helpfulness to reviews, despite the difficulty of the task. More than this, people perform this process systematically and consistently. Finally, we empirically identify the most relevant content features for machine learning prediction of review helpfulness.

Explaining and Predicting Helpfulness & Funniness of Online Reviews on the Steam Platform

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

Do Hedonic or Utilitarian Types of Online Product Reviews Make Reviews More Helpful?

Online sales can be influenced significantly by customer reviews, and thus there are several studies on what makes an online review helpful to consumers. However, none of those researches address the review helpfulness in the context of hedonic and utilitarian review types. This study examines how product type (hedonic and utilitarian) moderates the relationship between the level of review type (hedonicity and utilitarianity) and review helpfulness. To test the moderating effects, customer review data for perfume and bar soap product was collected from Amazon.com and analyzed by using a text-mining tool (QDA Miner) and a structural equation modeling software (AMOS 22.0). The results of this study indicate that the product type moderates the impact of the review type on review helpfulness when product type and review type are incongruent. Further, results show that the number of sentences in a customer review affects the review helpfulness when product type is utilitarian.

Do Hedonic or Utilitarian Types of Online Product Reviews Make Reviews More Helpful?

Online sales can be influenced significantly by customer reviews, and thus there are several studies on what makes an online review helpful to consumers. However, none of those researches address the review helpfulness in the context of hedonic and utilitarian review types. This study examines how product type (hedonic and utilitarian) moderates the relationship between the level of review type (hedonicity and utilitarianity) and review helpfulness. To test the moderating effects, customer review data for perfume and bar soap product was collected from Amazon.com and analyzed by using a text-mining tool (QDA Miner) and a structural equation modeling software (AMOS 22.0). The results of this study indicate that the product type moderates the impact of the review type on review helpfulness when product type and review type are incongruent. Further, results show that the number of sentences in a customer review affects the review helpfulness when product type is utilitarian.