Mobility Network Evaluation in the User Perspective: Real-Time Sensing of Traffic Information in Twitter Messages

Kokkinogenis, Zafeiris; Filguieras, João; Carvalho, Sara; Sarmento, Luís; Rossetti, Rosaldo J. F.

doi:10.1016/b978-0-12-397041-1.00012-1

Cited by 21 publications

(25 citation statements)

References 22 publications

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“…Microblogging websites are valuable sources for sentiment analysis as users tend to share their opinions on different subjects [18]. In recent years, people use social media to comment and complaint about events and problems of their social life [5]. This information can be beneficial to support traffic management agencies for two reasons.…”

Section: ) User Emotion Analysis For Incident Detectionmentioning

confidence: 99%

“…By detecting the level of stress within a traffic related tweet, agencies can identify the factors causing non-recurrent congestions. Apart from [5], there is a general lack of research in incorporating user emotions for traffic related purposes.…”

Section: ) User Emotion Analysis For Incident Detectionmentioning

confidence: 99%

“…SVM has gained popularity as one of the most efficient machine learning algorithms. In fact, several authors in the literature has successfully used SVM as their text classification algorithm [5,9,[19][20][21].…”

Section: Tweet Classifiermentioning

confidence: 99%

“…Unlike road sensors, there is no cost involved in using Twitter as the company offers free access to a subset of their data. Moreover, while algorithmic incident detection only considers changes in frequency counts, Twitter users can describe a wide variety of traffic events [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Traffic event detection framework using social media

Salas

Georgakis

Nwagboso

et al. 2017

2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC)

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Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.

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Section: ) User Emotion Analysis For Incident Detectionmentioning

confidence: 99%

Section: ) User Emotion Analysis For Incident Detectionmentioning

confidence: 99%

Section: Tweet Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Traffic event detection framework using social media

Salas

Georgakis

Nwagboso

et al. 2017

2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC)

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“…Therefore, picking up the available technology and harnessing the huge quantity of data about peoples sentiment, preferences, thinking and opinions available in OSN, we propose a novel framework for the development of new products and services in transportation using an open innovation approach, based on automatic content analysis of social media data. This work follows up previous effort to explore the pontential use of social media in the domain of traffic and transportation engineering [32,33,34].…”

Section: Introductionmentioning

confidence: 99%

Mining social media for open innovation in transportation systems

Ulloa

Saleiro

Rossetti

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.

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