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Integration of Different Analytical Concepts on Multimedia Contents in Service of Intelligent Knowledge Extraction

Integration of Different Analytical Concepts on Multimedia Contents in Service of Intelligent Knowledge Extraction

Goran Klepac
Copyright: © 2017 |Pages: 30
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch102
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MLA

Klepac, Goran. "Integration of Different Analytical Concepts on Multimedia Contents in Service of Intelligent Knowledge Extraction." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 2493-2522. https://doi.org/10.4018/978-1-5225-1759-7.ch102

APA

Klepac, G. (2017). Integration of Different Analytical Concepts on Multimedia Contents in Service of Intelligent Knowledge Extraction. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 2493-2522). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch102

Chicago

Klepac, Goran. "Integration of Different Analytical Concepts on Multimedia Contents in Service of Intelligent Knowledge Extraction." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 2493-2522. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch102

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Abstract

This chapter will introduce methodology how to use analytical potential of multimedia contents like YouTube, Bing Videos or Vimeo for discovering behavioral consumer characteristics. Chapter will also show how to consolidate unstructured text data sources from blogs and Twitter with revealed knowledge from multimedia contents for better understanding consumer habits and needs. For this purposes Social Network Analysis will be used as well as text mining techniques on different internet data sources. Presented methodology has practical value where information about customer behavior, preferences and changes in preferences during different time periods is valuable information for campaign planning, campaign management and new product development. Presented methodology also captures different techniques for data crawling from different internet resources, as well as analytical consolidation of revealed results which aim is better understanding of client behavior.

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