Statistical feature extraction for cross-language web content quality assessment

2011 ◽  
Author(s):  
Guang-Gang Geng ◽  
Xiao-Dong Li ◽  
Li-Ming Wang ◽  
Wei Wang ◽  
Shuo Shen
Keyword(s):  
Feature Extraction ◽  
Quality Assessment ◽  
Web Content ◽  
Content Quality ◽  
Statistical Feature ◽  
Cross Language

Statistical cross-language Web content quality assessment

2012 ◽  
Vol 35 ◽  
pp. 312-319 ◽  
Author(s):  
Guang-Gang Geng ◽  
Li-Ming Wang ◽  
Wei Wang ◽  
An-Lei Hu ◽  
Shuo Shen
Keyword(s):  
Quality Assessment ◽  
Web Content ◽  
Content Quality ◽  
Cross Language

A novel statistical feature extraction method for textual images: Optical font recognition

2012 ◽  
Vol 39 (5) ◽  
pp. 5470-5477 ◽  
Author(s):  
Bilal Bataineh ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Khairuddin Omar
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Statistical Feature ◽  
Font Recognition

Postharvest Quality Assessment of Non-Chemical Bananas Utilizing HOG Feature Extraction and K-means Clustering

2021 ◽  
Author(s):  
Stanley Glenn E. Brucal ◽  
Hannia O. Ditano ◽  
Napoleon C. Labuson ◽  
Shenelae Loreece D. Raton ◽  
Leonardo A. Samaniego ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Quality Assessment ◽  
Postharvest Quality

Style and fit customization: a web content mining approach to evaluate online mass customization experiences

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chunmin Lang ◽  
Sibei Xia ◽  
Chuanlan Liu
Keyword(s):  
Feature Extraction ◽  
Data Analysis ◽  
Mass Customization ◽  
Web Content ◽  
Content Type ◽  
Web Content Mining ◽  
Major Benefit ◽  
Content Mining ◽  
Emotional Value ◽  
Benefits And Costs

PurposeThis study intends to examine consumers' fashion customization experiences through a web content mining (WCM) approach. By applying the theory of customer value, this study explores the benefits and costs of two levels of mass customization (MC) to identify the values derived from style (i.e. shoe customization) and fit customization experiences (i.e. apparel customization) and further to compare the dominating dimensions of value derived across style and fit customization.Design/methodology/approachA WCM approach was applied. Also, two case studies were conducted with one focusing on style customization and the other focusing on fit customization. The brand Vans was selected to examine style customization in study 1. The brand Sumissura was selected to examine fit customization in study 2. Consumers' comments on customization experiences from these two brands were collected through social networks, respectively. After data cleaning, 394 reviews for Vans and 510 reviews for Sumissura were included in the final data analysis. Co-occurrence plots, feature extraction and grouping were used for the data analysis.FindingsThe emotional value was found to be the major benefit for style customization, while the functional value was indicated as the major benefit for fit customization, followed by ease of use and emotional value. In addition, three major themes of costs, including unsatisfied service, disappointing product performance and financial risk, were revealed by excavating and evaluating consumers' feedback of their actual clothing customization experiences with Sumissura.Originality/valueThis study initiates the effort to use web mining, specifically, the WCM approach to thoroughly investigate the benefits and costs of MC through real consumers' feedback of two different types of fashion products. The analysis of this study also reflects the levels of customization: style and fit. It provides an in-depth text analysis of online MC consumers' feedback through the use of feature extraction analysis and word co-occurrence networks.

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