Semi-supervised text mining for dynamic business network discovery

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Pacific Asia Conference on Information Systems, PACIS 2012
PublisherPacific Asia Conference on Information Systems
StatePublished - 2012

Conference

Title16th Pacific Asia Conference on Information Systems, PACIS 2012
PlaceViet Nam
CityHo Chi Minh City
Period11 - 15 July 2012

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.

Research Area(s)

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

Citation Format(s)

Semi-supervised text mining for dynamic business network discovery. / Zhang, Wenping; Cai, Yi; Lau, Raymond Y.K.; Liao, Stephen S.; Kwok, Ron C.W.

Proceedings - Pacific Asia Conference on Information Systems, PACIS 2012. Pacific Asia Conference on Information Systems, 2012.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review