A Convolution Neural Network-Based Representative Spatio-Temporal Documents Classification for Big Text Data

With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is being actively conducted. However, if spatiotemporal information that does not describe the core subject of a document is extracted, it is rather difficult to guarantee the accuracy of core event analysis. Therefore, it is important to extract spatiotemporal information that describes the core topic of a document. In this study, spatio-temporal information describing the core topic of a document is defined as 'representative spatio-temporal information', and documents containing representative spatiotemporal information are defined as 'representative spatio-temporal documents'. We proposed a character-level Convolution Neuron Network (CNN)-based document classifier to classify representative spatio-temporal documents. To train the proposed CNN model, 7400 training data were constructed for representative spatio-temporal documents. The experimental results show that the proposed CNN model outperforms traditional machine learning classifiers and existing CNN-based classifiers.