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

Rating and perceived helpfulness in a bipartite network of online product reviews

In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category “musical instruments”, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses’ improvement of the review service management to support customers’ experiences and online customers’ decision-making.publishe

A Novel Approach to Predict the Helpfulness of Online Reviews

Online reviews help consumers reduce uncertainty and risks faced in purchase decision making by providing information about products and services. However, the overwhelming amount of data continually being produced in online review platforms introduce a challenge for customers to read and judge the reviews. This research addresses the problem of misleading and overloaded information by developing a novel approach to predict the helpfulness of online reviews. The proposed approach in this study, first, clusters reviews using reviewer-related, and temporal factors. It then uses review-related factors to predict online review helpfulness in each cluster. Using a sample of Amazon.com reviews, the empirical findings offer strong support to the proposed approach and show its superior predictions of review helpfulness compared to earlier approaches. The outcomes of this study help customers in online shopping and assist online retailers in reducing information overload to improve their customers’ experience

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

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

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