CASIA OpenIR  > 复杂系统管理与控制国家重点实验室
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches
Chen, Yuanyuan1,2; Lv, Yisheng1,3; Wang, Xiao1,3; Li, Lingxi4; Wang, Fei-Yue1,3
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2019-08-01
Volume20Issue:8Pages:3049-3058
Corresponding AuthorLv, Yisheng(yisheng.lv@ia.ac.cn)
AbstractMining 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.
KeywordDeep learning social transportation traffic information detection social media text mining
DOI10.1109/TITS.2018.2871269
WOS KeywordNEURAL-NETWORK ; TRANSPORTATION ; TWITTER ; ISSUE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71232006] ; National Natural Science Foundation of China[61233001]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000478948000020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:30[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25773
Collection复杂系统管理与控制国家重点实验室
Corresponding AuthorLv, Yisheng
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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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