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Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
[article]
2016
arXiv
pre-print
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However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. ...
In this work, we introduce neural network based classification methods for binary and multi-class tweet classification task. ...
All of these modules can be built on top of our classification system and work in concert with it. Figure 1 : 1 Convolutional neural network on a tweet. ...
arXiv:1608.03902v1
fatcat:632knvdypnf5za6xi26kiavfnm
Applications of Online Deep Learning for Crisis Response Using Social Media Information
[article]
2016
arXiv
pre-print
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We test our models using a crisis-related real-world Twitter dataset. ...
In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes ...
RELATED WORK Recent studies have shown the usefulness of crisis-related data on social media for disaster response and management [1, 22, 24] . ...
arXiv:1610.01030v2
fatcat:fekss7cixfarjg7lbajt3hzwpa
Identifying Informative Messages in Disasters using Convolutional Neural Networks
2016
International Conference on Information Systems for Crisis Response and Management
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Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the "bag of words" and n-grams as features on several datasets of messages from ...
Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the National ...
dblp:conf/iscram/CarageaST16
fatcat:fbcmlz4h7jhjfl3nplpf3f3un4
Sportsman's Mental State Evaluation and Early Warning Method Based on Intelligent CNN
2022
Scientific Programming
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This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling ...
For data processing, behavioral features, attribute features, content features, and social relationship features are extracted from text information as the input of the CNN. ...
to provide timely intervention. e mapping relationship m used in this paper is a convolutional neural network (CNN) in deep learning. ...
doi:10.1155/2022/4711490
fatcat:lgedhhamtvejhfxvgv7eizhske
Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model
2022
Computational Intelligence and Neuroscience
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The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network. ...
In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior. ...
So, this proposed work has used the limited number of convolution neural network for the extraction of the features and later on recurrent neural network is used for performing the classification. ...
doi:10.1155/2022/9404242
pmid:35378814
pmcid:PMC8976617
fatcat:cidrc2lnnvg7bcjvetcaum4qhy
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
[article]
2020
arXiv
pre-print
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In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. ...
Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. ...
of multimodal analysis of the crisis-related social media data. ...
arXiv:2004.11838v1
fatcat:t46ww2jm2zauffn2g7fdkhef6u
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
[article]
2018
arXiv
pre-print
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During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness ...
However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. ...
(Johnson and Zhang 2015) use a Convolutional Neural Networks (CNN) via region embedding in which the CNN learns a small region from embedding. Miyato et al. ...
arXiv:1805.06289v1
fatcat:4iomqtnrknb2hbvdtt3khgx3va
Using Convolution Neural Network for Defective Image Classification of Industrial Components
2021
Mobile Information Systems
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For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task. ...
Computer vision provides effective solutions in many imaging relation problems, including automatic image segmentation and classification. ...
Introduction With the rapid development of Internet technology and media, the image data spread on the Internet is growing exponentially every day. ...
doi:10.1155/2021/9092589
fatcat:sep3lpggzfbglfbjuofu3cysry
Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach
2019
International Conference on Information Systems for Crisis Response and Management
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Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data. ...
This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model. ...
Thanks to Gabriele Mantovani Banella and Sasa Vranic for helping in the development of web layers and web services for the integration of SMFR into the existing EFAS/GloFAS systems. ...
dblp:conf/iscram/LoriniCDKNS19
fatcat:2oxyyrfllzfodgavnu7twqwoyy
The Application of Artificial Intelligence Decision-Making Algorithm in Crisis Analysis and Optimization of the International Court System
2022
Mobile Information Systems
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Therefore, this study proposes a fusion CNN-GRU network result model that uses a text classification neural network structure convolutional neural network with a good effect to combine the GRU neural network ...
Promote the development of the connotation of smart courts through the collaborative integration of artificial intelligence technology and system to promote the deep integration of judicial theory and ...
Acknowledgments is work was supported by the e Project of Baoding City Science and Technology Bureau (Research on the construction strategy of Baoding grassroots smart court under the background of AI, ...
doi:10.1155/2022/8150122
fatcat:mcd56gfxznfprox4zxs5eckywq
Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach
[article]
2019
arXiv
pre-print
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Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data. ...
This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model. ...
Thanks to Gabriele Mantovani Banella and Sasa Vranic for helping in the development of web layers and web services for the integration of SMFR into the existing EFAS/GloFAS systems. ...
arXiv:1904.10876v1
fatcat:yvzbrxn6wrf23ixfvse7r5ocba
Microblog Sentiment Classification via Recurrent Random Walk Network Learning
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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We propose a novel recurrent random walk network learning framework for the problem by exploiting both users' posted tweets and their social relations in microblogs. ...
In this paper, we consider the problem of microblog sentiment classification from the viewpoint of heterogeneous MSC network embedding. ...
Table 1 : 1 Experimental results on microblog sentiment classification
using STS dataset with different proportions of training data. ...
doi:10.24963/ijcai.2017/494
dblp:conf/ijcai/ZhaoLCHZ17
fatcat:qmdeox7u5vgpnmbfmdylfcpcvq
Research on Fine-Grained Classification of Rumors in Public Crisis ——Take the COVID-19 incident as an example
2020
E3S Web of Conferences
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[Method / Process] Based on the rumor data of several mainstream rumor refuting platforms, the pre-training model of BERT was used to fine-tuning in the context of COVID-19 events to obtain the feature ...
of rumors about COVID-19 based on the BERT model. ...
in convolutional neural networks. ...
doi:10.1051/e3sconf/202017902027
fatcat:3n6nv7tiyncvffhbvj6s3to5ne
Public health face mask detection of Covid-19 utilizing convolutional neural network (CNN)
2022
AIP Conference Proceedings
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More specifically, Neural Networks' Convolutional learning algorithm uses the extraction of features from imagery that would be further studied by several hidden layers. ...
Face mask detection is commonly acknowledged as the detection of whether a person wears a mask. The study applied the Convolutional Neural Networks (CNN) method. ...
ACKNOWLEDGMENTS Author would like to thank Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM), the internal Engineering Faculty, and the Department of Electrical Engineering, University of Muhammadiyah ...
doi:10.1063/5.0094485
fatcat:qd6umgrh2re2xoeidxbhxyhejy
Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM
2021
Computational Intelligence and Neuroscience
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In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of ...
Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation ...
Acknowledgments is study was supported by the Faculty of International Studies in Henan Normal University. ...
doi:10.1155/2021/2578422
fatcat:crkf7pazx5gt3gjjwc5wf62ukq
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