Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

doi:10.1109/TITS.2018.2871269

	Detecting Traffic Information From Social Media Texts With Deep Learning Approaches
	Chen, Yuanyuan1,2 ; Lv, Yisheng1,3 ; Wang, Xiao1,3 ; Li, Lingxi 4; Wang, Fei-Yue1,3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2019-08-01
卷号	20 期号:8 页码:3049-3058
通讯作者	Lv, Yisheng(yisheng.lv@ia.ac.cn)
摘要	Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-ofword model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.
关键词	Deep learning social transportation traffic information detection social media text mining
DOI	10.1109/TITS.2018.2871269
关键词[WOS]	NEURAL-NETWORK ; TRANSPORTATION ; TWITTER ; ISSUE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71232006] ; National Natural Science Foundation of China[61233001] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71232006] ; National Natural Science Foundation of China[61233001]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000478948000020
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：60[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25773
专题	复杂系统管理与控制国家重点实验室
通讯作者	Lv, Yisheng
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Shandong, Peoples R China 4.Indiana Univ Purdue Univ, Dept Elect & Comp Engn, Indianapolis, IN 46202 USA
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chen, Yuanyuan,Lv, Yisheng,Wang, Xiao,et al. Detecting Traffic Information From Social Media Texts With Deep Learning Approaches[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2019,20(8):3049-3058.
APA	Chen, Yuanyuan,Lv, Yisheng,Wang, Xiao,Li, Lingxi,&Wang, Fei-Yue.(2019).Detecting Traffic Information From Social Media Texts With Deep Learning Approaches.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,20(8),3049-3058.
MLA	Chen, Yuanyuan,et al."Detecting Traffic Information From Social Media Texts With Deep Learning Approaches".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20.8(2019):3049-3058.

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