Real-time event detection using recurrent neural network in social sensors
We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual da...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719856492 |
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| _version_ | 1832553258822926336 |
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| author | Van Quan Nguyen Tien Nguyen Anh Hyung-Jeong Yang |
| author_facet | Van Quan Nguyen Tien Nguyen Anh Hyung-Jeong Yang |
| author_sort | Van Quan Nguyen |
| collection | DOAJ |
| description | We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores. |
| format | Article |
| id | doaj-art-5958167fd1284aefa6c21f41f94d3319 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5958167fd1284aefa6c21f41f94d33192025-02-03T05:54:32ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-06-011510.1177/1550147719856492Real-time event detection using recurrent neural network in social sensorsVan Quan NguyenTien Nguyen AnhHyung-Jeong YangWe proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores.https://doi.org/10.1177/1550147719856492 |
| spellingShingle | Van Quan Nguyen Tien Nguyen Anh Hyung-Jeong Yang Real-time event detection using recurrent neural network in social sensors International Journal of Distributed Sensor Networks |
| title | Real-time event detection using recurrent neural network in social sensors |
| title_full | Real-time event detection using recurrent neural network in social sensors |
| title_fullStr | Real-time event detection using recurrent neural network in social sensors |
| title_full_unstemmed | Real-time event detection using recurrent neural network in social sensors |
| title_short | Real-time event detection using recurrent neural network in social sensors |
| title_sort | real time event detection using recurrent neural network in social sensors |
| url | https://doi.org/10.1177/1550147719856492 |
| work_keys_str_mv | AT vanquannguyen realtimeeventdetectionusingrecurrentneuralnetworkinsocialsensors AT tiennguyenanh realtimeeventdetectionusingrecurrentneuralnetworkinsocialsensors AT hyungjeongyang realtimeeventdetectionusingrecurrentneuralnetworkinsocialsensors |