Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2017

Abstract

Efficient and commuter friendly public transportation system is a critical part of a thriving and sustainable city. As cities experience fast growing resident population, their public transportation systems will have to cope with more demands for improvements. In this paper, we propose a crowdsensing and analysis framework to gather and analyze realtime commuter feedback from Twitter. We perform a series of text mining tasks identifying those feedback comments capturing bus related micro-events; extracting relevant entities; and, predicting event and sentiment labels. We conduct a series of experiments involving more than 14K labeled tweets. The experiments show that incorporating domain knowledge or domain specific labeled data into text analysis methods improves the accuracies of the above tasks. We further apply the tasks on nearly 200M public tweets from Singapore over a six month period to show that interesting insights about bus services and bus events can be derived in a scalable manner.

Keywords

Classification, Crowdsensing, Information extraction, Micro-events analysing, Sentiment analysis, Transportation

Discipline

Computer Sciences | Databases and Information Systems

Publication

2016 IEEE International Conference on Big Data

First Page

2157

Last Page

2166

ISBN

9781467390040

Identifier

10.1109/BigData.2016.7840845

Publisher

Institute of Electrical and Electronics Engineers Inc.

City or Country

Washington

Additional URL

http://doi.org/10.1109/BigData.2016.7840845

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