Real-time event detection using recurrent neural network in social sensors. (June 2019)

• Record Type: Journal Article
• Authors: Nguyen, Van Quan; Anh, Tien Nguyen; Yang, Hyung-Jeong
• Is Part Of: International journal of distributed sensor networks. Volume 15:Number 6(2019)
• Publication Date: 2019-06
• Subjects: Social data -- neural network -- multiple word embedding -- event detection -- long short-term memory -- real-time; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147719856492
• Languages: English
• ISSNs: 1550-1329

Abstract

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.