Measuring and Visualizing Transit Customers’ Satisfaction Using Twitter Data

The feasibility of utilizing Twitter data for the purposes of measuring and visualizing transit customers’ satisfaction is evaluated based on a series of analyses: data mining, semantic analysis, sentiment analysis, and GIS visualization. With free access (under certain restrictions) to Twitter databases through Twitter API, search modifications can be made to adjust the needs of the developer. Twitter data used in this study is acquired through a unique search combination, with keywords such as agency name and mode choice accompanied with a search area and a language. This methodology can collect all kind of tweets related to public transportation, with or without an agency name contained in a text. Further, semantic analysis was applied to classify and store each tweet into corresponding category based on a high volume lexical analysis. In order to quantify service quality, sentiment analysis was used as a customer satisfaction measurement system that grades a tweet from extremely negative to extremely positive. In the end, tweets were visualized given their locations for problem detection and identification.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AP000 Public Transportation Group.
• Authors:
  • Wu, Baocheng
  • Idris, Ahmed Osman
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 18p

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Classification; Cluster analysis; Customer satisfaction; Data analysis; Data mining; Geographic information systems; Public transit; Quality of service; Social media; Visualization
• Identifier Terms: Twitter
• Uncontrolled Terms: Text mining
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01659975
• Record Type: Publication
• Report/Paper Numbers: 18-05028
• Files: TRIS, TRB, ATRI
• Created Date: Feb 13 2018 9:53AM