Semi-supervised text mining for dynamic business network discovery

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

5 Citations (Scopus)

Abstract

Recently, much research effort has been devoted to the discovery and analysis of online social networks. However, relatively little research has been done for business network discovery and analysis. Although named entity recognition (NER) tools are available to identify basic entities in texts, there are still challenging research problems, such as co-reference resolution and the identification of abbreviations of organization names. Guided by the design science methodology, the main contribution of this paper is the design and development of a novel semi-supervised method for the identification of business entities (e.g., companies) and their relationships. Based on the automatically mined business networks, financial analysts can then predict the business prestige of companies for better financial investment decision making. Initial experiments show that the proposed business entity identification method is more effective than other baseline methods. Moreover, the proposed semi-supervised business relationship mining method is more effective than the state-of-theart supervised machine learning classifier when a large number of manually labeled training examples are not available. The managerial implication is that business managers can apply the design artifacts to promptly identify potential business partners and competitors, and hence improve their strategic business decision marking processes.
Original languageEnglish
Title of host publicationProceedings - Pacific Asia Conference on Information Systems, PACIS 2012
PublisherPacific Asia Conference on Information Systems
Publication statusPublished - 2012
Event16th Pacific Asia Conference on Information Systems, PACIS 2012 - Ho Chi Minh City, Viet Nam
Duration: 11 Jul 201215 Jul 2012
http://aisel.aisnet.org/pacis2012/

Conference

Conference16th Pacific Asia Conference on Information Systems, PACIS 2012
Country/TerritoryViet Nam
CityHo Chi Minh City
Period11/07/1215/07/12
Internet address

Research Keywords

  • Business Network Mining
  • Named Entity Recognition
  • Statistical Learning
  • Text Mining

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