Text Analytics for Supporting Stakeholder Opinion Mining for Large-scale Highway Projects

Research output: Contribution to journalConference article

Abstract

For large-scale highway projects, late identification of stakeholder concerns often leads to design changes and duplication of effort, which may cause major project delays. This paper proposes a stakeholder opinion mining approach for helping transportation practitioners better identify the types of concerns in the early project stage. The proposed approach includes two major components: (1) stakeholder concern extraction, and (2) stakeholder concern classification. This paper focuses on presenting the proposed methodology and experimental results for stakeholder concern extraction, which extracts the words and phrases that describe stakeholder concerns from stakeholder comments on large-scale highway projects. In developing the proposed stakeholder concern extraction methodology, several supervised machine learning (ML) algorithms were tested and evaluated, and the effect of using a predefined name list as feature was also investigated. All the algorithms were tested on a testing data set of 200 comment sentences, which were selected from a comment collection including 1,849 stakeholder comments on five large-scale highway projects.

Original languageEnglish (US)
Pages (from-to)518-524
Number of pages7
JournalProcedia Engineering
Volume145
DOIs
StatePublished - Jan 1 2016
EventInternational Conference on Sustainable Design, Engineering and Construction, ICSDEC 2016 - Tempe, United States
Duration: May 18 2016May 20 2016

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Learning algorithms
Learning systems
Testing

Keywords

  • Data analytics
  • Information extraction
  • Machine learning
  • Natural language processing
  • Opinion mining

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Text Analytics for Supporting Stakeholder Opinion Mining for Large-scale Highway Projects. / Lv, Xuan; El-Gohary, Nora.

In: Procedia Engineering, Vol. 145, 01.01.2016, p. 518-524.

Research output: Contribution to journalConference article

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