A Novel Transit Rider Satisfaction Metric: Rider Sentiments Measured from Online Social Media Data

Collins, Craig; Hasan, Samiul; Ukkusuri, Satish V.

doi:10.5038/2375-0901.16.2.2

Cited by 148 publications

(95 citation statements)

References 13 publications

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“…Girardin et al (2009) evaluated urban attractiveness by analyzing images from Flickr and mobile phone usage data, while Sun, Fan, Bakillah and Zipf (2015) used geo-tagged images for road-based travel recommendations. As another approach, several researchers improved and refined various methodologies for extracting emotions (Resch et al, 2016), transit rider satisfaction (Collins, Hasan, & Ukkusuri, 2013), and community happiness (Quercia et al, 2012) from Twitter data, also combined with demographics and other objective characteristics of a place such as education or obesity (Mitchell, Frank, Harris, Dodds, & Danforth, 2013), or even defined sentiment as a function of movement . The advantages of utilizing available additional datasets such as demographics, mobile phone data or mobility trajectories are twofold; they can help the interpretation of the primary results extracted from social media, and, on the other hand, they are also appropriate for validation purposes.…”

Section: Citizen-contributed Geographic Information To Describe Urbanmentioning

confidence: 99%

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

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The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

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Section: Citizen-contributed Geographic Information To Describe Urbanmentioning

confidence: 99%

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

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“…There are many advantages in using sentiment analysis to measure transit rider satisfaction (instead of transit surveys): minimal cost of data collection, data collected in real time, user-specific needs can be assessed, and the data can provide context to why a particular sentiment is felt. Sentiment analysis could provide customer feedback on fare increases, services and safety changes due to a lack of personnel (Collins, Hasan & Ukkusuri, 2013). One program for sentiment analysis is SentiStrength, a machine-learning program that identifies the sentiment value of a short text by quantifying the general strength of the sentiment behind each text and average negative and positive sentiment.…”

Section: Social Media Performance Metricsmentioning

confidence: 99%

Measuring the Impacts of Social Media on Advancing Public Transit

Liu¹,

Ban²,

Elrahman³

2017

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Type of Report and Period CoveredFinal 14. Sponsoring Agency Code Supplementary Notes AbstractThis project is a collaboration between Portland State University (PSU) and the Center for Infrastructure Transportation & Environment (CITE) at Rensselaer Polytechnic Institute seeking to develop performance measures for assessing the impacts of social media on promoting public transit. Revolutionary changes have occurred in the communication landscape, and there has been a rapid diffusion of social media use as a means of communicating transit information to the public. Significant resources are being directed to the use of social media in communication, yet little effort exists that measures the impacts of these popular vehicles of communication. Rarely studied is the role of social media in achieving the overarching goals of advancing the mission of transit agencies through increasing recruitment and retention of transit riders; increasing resources and customer satisfaction; addressing system issues, performance efficiency and effectiveness; and improving employee productivity and morale. There is a need to measure the impacts of social media and account for the cost effectiveness of its wide use as a means of communication in public transit agencies. This research intends to extend understanding about whether investments in social media tools effectively achieve their intended purposes.The goal of this project is to identify social media-related measures for public transit agencies that can comprehensively capture the impacts of social media use on agency performance. This project will rely on a review of the research and practice literature to document findings in other fields on performance measures used to assess the impacts of social media. Best practices will be identified. A survey of a random sample of public transit agencies nationwide will target identifying performance measures currently used to document impacts. Gaps will be identified. Investigators will compare and contrast measures used in transit agencies and other fields and identify a list of performance measures that are most effective for transit agencies.

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“…Collines et al [11] measured public transport rider satisfaction toward transit system services using the riders' tweets on Twitter. This research helped the transit system to improve the service quality and safety monitoring by adding more personnel.…”

Section: Semantic Sentiment Analysismentioning

confidence: 99%