Customer segmentation by web content mining

2021 ◽  
Vol 61 ◽  
pp. 102588
Author(s):  
Jinfeng Zhou ◽  
Jinliang Wei ◽  
Bugao Xu
Keyword(s):  
Customer Segmentation ◽  
Web Content ◽  
Web Content Mining ◽  
Content Mining

Multilingual Web Content Mining

2004 ◽  
pp. 88-100
Author(s):  
Rowena Chau ◽  
Chung-Hsing Yeh
Keyword(s):  
Information Filtering ◽  
User Profile ◽  
Linguistic Knowledge ◽  
Web Content ◽  
Self Organizing Maps ◽  
Web Documents ◽  
Web Content Mining ◽  
Concept Space ◽  
Content Mining ◽  
Multilingual Text

This chapter presents a novel user-oriented, concept-based approach to multilingual web content mining using self-organizing maps. The multilingual linguistic knowledge required for multilingual web content mining is made available by encoding all multilingual concept-term relationships using a multilingual concept space. With this linguistic knowledge base, a concept-based multilingual text classifier is developed. It reveals the conceptual content of multilingual web documents and forms concept categories of multilingual web documents on a concept-based browsing interface. To personalize multilingual web content mining, a concept-based user profile is generated from a user’s bookmark file to highlight the user’s topics of information interest on the browsing interface. As such, both explorative browsing and user-oriented, concept-focused information filtering in multilingual web are facilitated.

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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