Credibility Analysis for Online Product Reviews

2018 ◽  
Vol 9 (3) ◽  
pp. 37-54
Author(s):  
Min Chen ◽  
Anusha Prabakaran
Keyword(s):  
Anomaly Detection ◽  
Data Visualization ◽  
Opinion Mining ◽  
Spam Detection ◽  
Product Reviews ◽  
Purchasing Decisions ◽  
Online Product Reviews ◽  
Level Data ◽  
High Level ◽  
Fake Reviews

With the prevalence of e-commerce, online product reviews are increasingly considered crowd-sourced consumer opinions that significantly influence customer purchasing decisions and product rankings. It is therefore important to ensure the truthfulness of reviews by detecting and filtering out fake/spam reviews. This article presents an effective framework to analyze review credibility for spam detection and opinion mining. It incorporates three methods: duplicated review detection, anomaly detection, and incentivized review detection, that complement each other to produce statistical credibility scores indicating review credibility. A practical end-to-end system is designed and developed accordingly, and is equipped with high-level data visualization for easy interpretation and summarization of the analysis results. Experiments on an Amazon review dataset demonstrate its efficiency, scalability and accuracy. This system could help e-commerce and consumers identify fake reviews, refine product rankings, and constrain vendors and spammers from engaging in dishonest practices.

Describing the User Experience of Wearable Fitness Technology through Online Product Reviews

2016 ◽  
Vol 60 (1) ◽  
pp. 1073-1077 ◽  
Author(s):  
Jessica R. Michaelis ◽  
Michael A. Rupp ◽  
James Kozachuk ◽  
Baotran Ho ◽  
Daniela Zapata-Ocampo ◽  
...  
Keyword(s):  
Cost Effective ◽  
The United States ◽  
Stepwise Multiple Regression ◽  
Great Promise ◽  
Product Reviews ◽  
Online Product Reviews ◽  
Fitness Tracking ◽  
Acceptance Rates ◽  
High Level ◽  
Tracking Devices

Regular exercise has many health benefits, however a major problem in the United States is that Americans do not exercise enough to reap these advantages. Although there are many ways that one can be motivated to exercise, the use of wearable technologies such as fitness tracking devices show great promise as an individual, and cost effective solution. On the other hand, many people who try out these devices return them leading to lower than idea acceptance rates for these devices. We examined online product reviews for wearable fitness devices in order to discover which factors led to product acceptance or rejection. We performed a qualitative analysis of user reviews across many websites and devices followed by a quantitative exploratory analysis using stepwise multiple regression predicting users’ experience. Overall, our results support that four high-level themes: usability, trust, motivation, and wearability determined a user’s experience.

scholarly journals Corpus Analysis and Annotation for Helpful Sentences in Product Reviews

2018 ◽  
Vol 11 (2) ◽  
pp. 76 ◽  
Author(s):  
Hana Almagrabi ◽  
Areej Malibari ◽  
John McNaught
Keyword(s):  
Learning Task ◽  
Corpus Analysis ◽  
Supervised Machine Learning ◽  
Product Reviews ◽  
Annotation Scheme ◽  
Online Product Reviews ◽  
High Level ◽  
Document Level

For the last two decades, various studies on determining the quality of online product reviews have been concerned with the classification of complete documents into helpful or unhelpful classes using supervised learning methods. As in any supervised machine-learning task, a manually annotated corpus is required to train a model. Corpora annotated for helpful product reviews are an important resource for the understanding of what makes online product reviews helpful and of how to rank them according to their quality. However, most corpora for helpfulness are annotated on the document level: the full review. Little attention has been paid to carrying out a deeper analysis of helpful comments in reviews. In this article, a new annotation scheme is proposed to identify helpful sentences from each product review in the dataset. The annotation scheme, guidelines and the inter-annotator agreement scores are presented and discussed. A high level of inter-annotator agreement is obtained, indicating that the annotated corpus is suitable to support subsequent research.

Survey on Review Spam of E-Commerce Sites

2014 ◽  
Vol 631-632 ◽  
pp. 1190-1193
Author(s):  
Sheng Xiu Yang ◽  
Lu Jie Fan
Keyword(s):  
Online Shopping ◽  
Important Determinant ◽  
Spam Detection ◽  
Product Reviews ◽  
Product Review ◽  
Review Spam ◽  
Quality Of Products ◽  
Fake Reviews ◽  
Potential Customers

Online shopping reviews provide valuable information for customers to compare the quality of products, and many other aspects of future purchases. People increasingly rely on information from E-commerce reviews. Product reviews is an important determinant of potential customers’ buying choices. However, spammers are joining this community to try to mislead consumers by writing fake or unfair reviews to confuse the consumers. Fake product review detection makes an attempt to detect fake reviews and remove them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on review spam. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect review spam are introduced and summarized. The trend of review spam detection is concluded finally.

Review Spam Detection by Highlighting Potential Spammers and Diminishing Their Effect

2018 ◽  
Vol 14 (1) ◽  
pp. 54-76
Author(s):  
Fatemeh Keshavarz ◽  
Ayeshaa Abdul Waheed ◽  
Btissam Rachdi ◽  
Reda Alhajj
Keyword(s):  
Opinion Mining ◽  
Spam Detection ◽  
Test Results ◽  
The Public ◽  
Group Activities ◽  
Review Spam ◽  
Fake Reviews ◽  
Potential Customers

Nowadays, millions of products and services are available to the public online. Therefore, searching for the best products which meets individuals' expectations would be difficult due to the existence of too many alternative choices. One of the most reliable approaches to choose a product or service is to exploit the experience of people who have already tried them, and are expected to have reported their almost honest opinions about them. A reviewing system is a place where individuals share their experience on products and services. Individuals may read and/or write their reviews which may be neutral and professional or biased. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and may be to watch their competitors. However, the popularity of reviewing systems ignites this motivation for some people to try to influence viewers by entering their fake reviews to promote some products or defame some others. These spam reviews should be detected and eliminated to prevent misleading potential customers and unethically affect the market. Opinion mining should be adapted to locate and eliminate potential spam reviews. In this paper, some review spam detection approaches have been proposed and examined over a sample dataset. The proposed approaches consider patterns that existed in trends of reviews, as well as reviewers' behavior. The approaches depend on various strategies such as observing abnormal trends, detecting uncommon or suspicious behaviors, investigating group activities, among others. The reported test results revealed some promising outcome.

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