Fake Product Review Monitoring and Removal for Genuine Online Product Reviews Using Opinion Mining

2017 ◽  
Vol 7 (1) ◽  
pp. 106-109
Author(s):  
◽  
Shaheen Jamil Khan ◽  
Tanvi Mirashi ◽  
Suraj Gupta ◽  
◽  
...  
Keyword(s):  
Opinion Mining ◽  
Product Reviews ◽  
Product Review ◽  
Online Product Reviews

Textual factors in online product reviews: a foundation for a more influential approach to opinion mining

2012 ◽  
Vol 12 (3) ◽  
pp. 301-330 ◽  
Author(s):  
Regan Robinson ◽  
Tiong-Thye Goh ◽  
Rui Zhang
Keyword(s):  
Opinion Mining ◽  
Product Reviews ◽  
Online Product Reviews

A Method of Extracting Opinion from Chinese Product Review

2011 ◽  
Vol 219-220 ◽  
pp. 1513-1517
Author(s):  
Rui Liu ◽  
Yi An ◽  
Lang Song
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Review ◽  
Sentiment Lexicon ◽  
Target Attribute

Automatic opinion mining and summarization from online reviews are very useful for customers and merchants. This paper proposes a method to extract opinions from Chinese product reviews. Firstly, reviews are pre-processed and the sentiment features are extracted based on a sentiment lexicon. Then, it finds out the matching target attribute using the extracted sentiment features base on the using co-occurrence knowledge of topic feature and sentiment feature. After the opinions were found, it generates the summary for products according to the most common opinions.

Opinion Mining and Product Review Summarization in E-Commerce

2020 ◽  
pp. 216-243
Author(s):  
Enakshi Jana ◽  
V. Uma
Keyword(s):  
User Experience ◽  
Opinion Mining ◽  
Online Shopping ◽  
Online Reviews ◽  
The Internet ◽  
Product Reviews ◽  
Product Review ◽  
Document Summarization ◽  
Review Summarization ◽  
Product Manufacture

With the immense increase of the number of users of the internet and simultaneously the massive expansion of the e-commerce platform, millions of products are sold online. To improve user experience and satisfaction, online shopping platform enables every user to give their reviews for each and every product that they buy online. Reviews are long and contain only a few sentences which are related to a particular feature of that product. It becomes very difficult for the user to understand other customer views about different features of the product. So, we need accurate opinion-based review summarization which will help both customers and product manufacture to understand and focus on a particular aspect of the product. In this chapter, the authors discuss the abstractive document summarization method to summarize e-commerce product reviews. This chapter has an in-depth explanation about different types of document summarization and how that can be applied to e-commerce product reviews.

A Fuzzy Domain Sentiment Ontology based Opinion Mining Approach for Chinese Online Product Reviews

2013 ◽  
Vol 8 (9) ◽  
Author(s):  
Hanshi Wang ◽  
Xinhui Nie ◽  
Lizhen Liu ◽  
Jingli Lu
Keyword(s):  
Opinion Mining ◽  
Product Reviews ◽  
Online Product Reviews

scholarly journals Product Review Mining for Opinion Using Ensemble Classifier

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
G. Vinodhini ◽  
RM. Chandrasekaran
Keyword(s):  
Support Vector Machine ◽  
Opinion Mining ◽  
Online Reviews ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Product Reviews ◽  
Operating Characteristics ◽  
Product Review ◽  
Review Mining ◽  
Suitable Form

Online product reviews is considered as a major informative resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volume of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve.   Key words: Opinion, Classification, Machine Learning.

scholarly journals Online Product Reviews: Effects of Star Ratings and Valence on Review Perception among Those High and Low in Need for Cognition

2019 ◽  
Vol 63 (1) ◽  
pp. 401-405
Author(s):  
Jacquelyn L. Schreck ◽  
Matthew G. Chin
Keyword(s):  
Purchase Intention ◽  
Need For Cognition ◽  
Product Reviews ◽  
Product Review ◽  
Intent To Purchase ◽  
Online Product Reviews ◽  
Online Product Review

This study seeks to understand online product review perception based on ratings, valence, and need for cognition. Review perception, review recollection, and intent to purchase after reading the reviews were being measured. Results showed that need for cognition had an effect on accuracy of review recognition and perceived review valence. Need for cognition and congruency (between rating of the review and valence of the review), as well as actual valence had an effect on perceived valence. Need for cognition, actual review valence, and review congruency all had an effect on purchase intention.

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.

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