Title page for etd-0621117-123439


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URN etd-0621117-123439
Author Jia-Wun Cai
Author's Email Address No Public.
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Department Information Management
Year 2016
Semester 2
Degree Master
Type of Document
Language English
Title Finding Potential Business through Text Mining Techniques Based on Automotive Industry
Date of Defense 2017-07-21
Page Count 53
Keyword
  • text mining
  • word2vec
  • enterprise performance
  • data envelopment analysis
  • Malmquist productivity index
  • Abstract In recent years, the breakthrough of science and technology has greatly changed human life. With the development of Internet and acceleration in global competition, economic globalization has become a major trend, and the influence of international organizations and multinational corporations are also becoming prominent. In terms of industrial development, single technologies can no longer meet the demand. Most innovations involve the technological integration across different domain. This study will focus on the analysis of technology collaboration across domain and international supply chain relation in automotive industry, to predict the promising Taiwanese automotive supply chain companies for the future.
    With the development of information technology, enterprise decision makers can use news media to guide investment decisions. In this study, text mining is used to extract “hot” terms of new technologies and products from the news. These words are then used to find others potentially related to them, by using word2vec to search for words semantically similar as these new technologies and products, i.e. extended terms. On the other hand, patents are undoubtedly the most complete technical documents available to the public. Each patent represents the output of research and development. We thus use the extended terms of new technologies and products as keywords to search for patents with documents containing these keywords in the USPTO database, and identify these Taiwanese supply chain companies with patents in USPTO database.
    This study adopts DEA-based Malmquist productivity index to evaluate the enterprise performance of supply chain companies. The experimental results show that the Taiwan companies identified with this method are indeed promising in their growth.
    Advisory Committee
  • Cheng-Jung Yang - chair
  • Chih-Hua Tai - co-chair
  • Keng-Pei Lin - advisor
  • Files
  • etd-0621117-123439.pdf
  • Indicate in-campus at 1 year and off-campus access at 5 year.
    Date of Submission 2017-07-21

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