A Convolutional Neural Network for Traffic Information Sensing from Social Media Text
Chen, Yuanyuan1,2; Lv, Yisheng1; Wang, Xiao1,3; Wang, Fei-Yue1,3
2017
会议名称2017 IEEE 20th International Conference on Intelligent Transportation Systems
页码1-6
会议日期Oct. 16-19, 2017
会议地点Japan
摘要
Mining social media data to obtain traffic relevant information is an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific issue in social media mining that concerns to extract traffic relevant microblogs from the Sina Weibo platform, which is the first and essential step to further extract detailed traffic information, such as the location of a traffic incident. It is transformed into a machine learning problem of short text classification. We employ deep neural networks to classify microblogs into traffic relevant and traffic irrelevant ones. More specifically, we firstly adopt the continuous bag-of-word (CBOW) model to learn word embedding representations based on the dataset of three billion unlabeled microblogs. Next we use a convolutional neural network (CNN) to learn the abstract features of traffic relevant and traffic irrelevant microblogs. The key advances in this paper are: use of semantics of words and deployment of deep neural networks to extract traffic information from social media text. Experiments show that the proposed deep learning method has superior performance over support vector machine (SVM) based method and multi-layer perceptron (MLP) based method.
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20173
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
作者单位1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Qingdao Academy of Intelligent Industries
推荐引用方式
GB/T 7714
Chen, Yuanyuan,Lv, Yisheng,Wang, Xiao,et al. A Convolutional Neural Network for Traffic Information Sensing from Social Media Text[C],2017:1-6.
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