United States Department of Transportation
i
Social Media Analysis for Transit Assessment
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2019-12-01
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[PDF-4.69 MB]
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Abstract:Stakeholders and transportation planners often use users’ feedback to assess transit services including ride hailing platforms and reflect them for future plans. Interestingly, social network services (SNS) provide such information in a large set of text by individuals exchanging event base attitude and sentiment. This information is very useful, however, these data are often unorganized and it is intractable to process this extremely large set of text data by human effort whose size is continuously increasing. In this regime, the authors collected ride hailing service relevant text data from Twitter and created a database, and developed a novel Deep Learning (DL) framework that processes and classifies sentences that will automatically categorize the texts uploaded by service users according to transportation service specific criteria. The authors' model uses multiple kernels for convolution to capture local context among neighboring words in texts and is simplified by summarizing parameters in traditional models using a kernel function. Using their DL model, the authors trained a classifier that identifies 1) to which transit service a text corresponds (e.g., reliability, mobility and cleanness), and 2) which sentiment the text contains (i.e., positive vs. negative). Its prediction performance is comparable to state-of-the-art DL methods but the authors' model converges much faster during training which means it trains much more efficiently. The authors expect that their framework will provide feedback for policy makers who explore communication and information technology to create strategies to improve system efficiency and transit ridership.
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