Probabilistic Ranking of Product Features from Customer Reviews

2011 ◽  
pp. 208-215 ◽  
Author(s):  
Lisette García-Moya ◽  
Henry Anaya-Sánchez ◽  
Rafel Berlanga ◽  
María José Aramburu
Keyword(s):  
Customer Reviews ◽  
Product Features

Intelligent Kano classification of product features based on customer reviews

CIRP Annals ◽  
2019 ◽  
Vol 68 (1) ◽  
pp. 149-152 ◽  
Author(s):  
Diandi Chen ◽  
Dawen Zhang ◽  
Ang Liu
Keyword(s):  
Customer Reviews ◽  
Product Features

scholarly journals Spammer group detection and diversification of customers’ reviews

2021 ◽  
Vol 7 ◽  
pp. e472
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Abid Ali ◽  
Faiza Iqbal ◽  
Ibrar Hussain ◽  
...  
Keyword(s):  
Real World ◽  
Online Reviews ◽  
Direct Impact ◽  
Review System ◽  
Detection Problem ◽  
Review Time ◽  
Public Opinions ◽  
Customer Reviews ◽  
Product Features ◽  
Group Detection

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

Improving Customer Experience Using Sentiment Analysis in E-Commerce

2017 ◽  
pp. 216-224 ◽  
Author(s):  
Vinay Kumar Jain ◽  
Shishir Kumar
Keyword(s):  
Social Media ◽  
Customer Relationship ◽  
Product Launch ◽  
Customer Reviews ◽  
Product Features ◽  
New Product Launch ◽  
Data Points ◽  
Media Platform ◽  
The Right ◽  
Media Data

In today's world, millions of online users post their opinions on product features, services, quality, benefits and other values of the products. These opinions or sentiment data generated via different communication mediums often include vital data points that can be fruitful for businesses in understanding customer experiences, products quality and services. The E-commerce companies considered social media platform for new product launch, promotion of products and features or establishing a successful business to customer relationship which produces great results. Analytics on this Social media data helps in identifying the customers in the right demographic, psychographic and lifestyle group. This chapter identifying important characteristics of customer reviews which help businesses houses to improve their marketing strategies.

Mining opinionated product features using WordNet lexicographer files

2016 ◽  
Vol 43 (6) ◽  
pp. 769-785 ◽  
Author(s):  
Saif A. Ahmad Alrababah ◽  
Keng Hoon Gan ◽  
Tien-Ping Tan
Keyword(s):  
Assessment Tool ◽  
Empirical Evaluation ◽  
Online Reviews ◽  
Product Reviews ◽  
Specific Domain ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Customer Perspective ◽  
Massive Number

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.

A Trustworthy Sort Method for Shopping Customer Reviews Based on Correlation Degree with Product Features

2014 ◽  
Vol 644-650 ◽  
pp. 5542-5547
Author(s):  
Chun Ling Ding ◽  
Guo Sun Zeng ◽  
Wei Wang ◽  
Ting Ting Huang
Keyword(s):  
Web Sites ◽  
Customer Reviews ◽  
Product Features ◽  
Correlation Degree

Nowadays, customers can freely express shopping thoughts and reviews in e-commerce web sites. It results that people may be lost in the massive shopping reviews and cannot distinguish trusted reviews. This paper carries out a study on the trustworthy reviews in e-commerce web sites. We use the technique of words similarity to analyze the correlation degree between product features and customer reviews contents, and then propose a trustworthy sort method for shopping customer reviews.

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