Detecting Traffic Information From Social Media Texts With Deep Learning Approaches
文献类型：期刊论文
| 作者 | Chen, Yuanyuan1,2 ; Lv, Yisheng1,3; Wang, Xiao1,3; Li, Lingxi4; Wang, Fei-Yue1,3
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2019-08-01 |
| 卷号 | 20期号:8页码:3049-3058 |
| 关键词 | Deep learning social transportation traffic information detection social media text mining |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2018.2871269 |
| 通讯作者 | Lv, Yisheng(yisheng.lv@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NEURAL-NETWORK ; TRANSPORTATION ; TWITTER ; ISSUE |
| 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] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000478948000020 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25773]  |
| 专题 | 自动化研究所_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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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