A framework to analyze social tagging and unstructured data

Amjed Al-Thuhli, Mohammed Al-Badawi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The involvement of human interactions with business processes through Enterprise Social Networks improves organizations performance. However, Enterprise Social Networks consist of massive amount of data in form of structure and unstructured data. Therefore, finding valuable information from these types of data is a challenging issue. Nevertheless, with the annotation that are available in form of social tagging, some challenges have been resolved. In this paper, we investigate the problem of using social tagging in order to socialize organization business processes. Specifically, we present a framework to analyze social tagging and unstructured data that are generated by users to recommend tasks and activities of any type of business processes based on hybrid method of clustering and text classification. The framework uses k-means algorithm to cluster tags datasets and term frequency-inverse document frequency to weight user's documents. The experiment results performed on a real case study shows the efficiency of the framework after validates its accuracy.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-53
Number of pages8
ISBN (Electronic)9781728172835
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes
Event3rd International Conference on Information and Computer Technologies, ICICT 2020 - San Jose, United States
Duration: Mar 9 2020Mar 12 2020

Publication series

NameProceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020

Conference

Conference3rd International Conference on Information and Computer Technologies, ICICT 2020
Country/TerritoryUnited States
CitySan Jose
Period3/9/203/12/20

Keywords

  • Business processes
  • Clustering
  • Enterprise social networks
  • Text mining
  • TF-IDF
  • Unstructured data

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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