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Structuring of Unstructured SNS Messages on Rail Services using Deep Learning Techniques

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2018, 23(7), pp.19-26
  • DOI : 10.9708/jksci.2018.23.07.019
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : June 22, 2018
  • Accepted : July 12, 2018
  • Published : July 31, 2018

JinGyu Park 1 HwaYeon Kim 1 Hyoung-Geun Kim 2 Tae-Ki Ahn 3 Hyunbean Yi 1

1한밭대학교
2유코아시스템
3한국철도기술연구원

Accredited

ABSTRACT

This paper presents a structuring process of unstructured social network service (SNS) messages on rail services. We crawl messages about rail services posted on SNS and extract keywords indicating date and time, rail operating company, station name, direction, and rail service types from each message. Among them, the rail service types are classified by machine learning according to predefined rail service types, and the rest are extracted by regular expressions. Words are converted into vector representations using Word2Vec and a conventional Convolutional Neural Network (CNN) is used for training and classification. For performance measurement, our experimental results show a comparison with a TF-IDF and Support Vector Machine (SVM) approach. This structured information in the database and can be easily used for services for railway users.

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