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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. [ABSTRACT FROM AUTHOR]