Applied Sciences, Vol. 13, Pages 4230: Machine Learning Based Representative Spatio-Temporal Event Documents Classification

Applied Sciences, Vol. 13, Pages 4230: Machine Learning Based Representative Spatio-Temporal Event Documents Classification

Applied Sciences doi: 10.3390/app13074230

Authors: Byoungwook Kim Yeongwook Yang Ji Su Park Hong-Jun Jang

As the scale of online news and social media expands, attempts to analyze the latest social issues and consumer trends are increasing. Research on detecting spatio-temporal event sentences in text data is being actively conducted. However, a document contains important spatio-temporal events necessary for event analysis, as well as non-critical events for event analysis. It is important to increase the accuracy of event analysis by extracting only the key events necessary for event analysis from among a large number of events. In this study, we define important 'representative spatio-temporal event documents' for the core subject of documents and propose a BiLSTM-based document classification model to classify representative spatio-temporal event documents. We build 10,000 gold-standard training datasets to train the proposed BiLSTM model. The experimental results show that our BiLSTM model improves the F1 score by 2.6% and the accuracy by 4.5% compared to the baseline CNN model.