Soft Computing

, Volume 22, Issue 21, pp 7005–7014 | Cite as

Chinese microblog users’ sentiment-based traffic condition analysis

  • Donglin Cao
  • Shuru Wang
  • Dazhen LinEmail author


With the increasing number of vehicles in China, traffic condition analysis is of great significance to urban planning and public administration. However, the state-of-the-art traffic condition analysis approaches mainly rely on sensors, which are high cost and limited coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on users’ sentiment in Chinese Microblog. This approach is a low-cost crowdsourcing solution. Firstly, we train the gated recurrent unit (GRU) model and generative adversarial networks to estimate the sentiment of Microblog with traffic information. Secondly, we calculate the Traffic Sentiment Index to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the conditional generative adversarial networks to generate robust features which are used as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 3.79%. Furthermore, by using the Traffic Sentiment Index, we build a traffic condition analysis system and predict the time and roads of traffic jams in 4 Chinese cities. The predication experiment shows similar results with Baidu map which uses a lot of mobile phones as sensors, and proves the low-cost characteristic and performance of our approach.


Traffic condition Sentiment analysis Microblog Traffic Sentiment Index Generative adversarial networks 



The authors would like to thank the reviewers for their invaluable comments and suggestions, which greatly helped to improve the presentation of this paper.


This work was partly supported by the Welsh Government and Higher Education Funding Council for Wales through the Sĺžr Cymru National Research Network for Low Carbon, Energy and Environment (NRN-LCEE), the Nature Science Foundation of China (No. 61402386, No. 61305061, No. 61502105, No. 61572409, No. 81230087 and No. 61571188), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201743), and Education and scientific research projects of young and middle-aged teachers in Fujian Province under Grand No. JA15075. Fujian Province 2011 Collaborative Innovation Center of TCM Health Management and Collaborative Innovation Center of Chinese Oolong Tea IndustryąłCollaborative Innovation Center (2011) of Fujian Province.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Cognitive Science DepartmentXiamen UniversityXiamenChina
  2. 2.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina

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