# Machine Learning Based Representative Spatio-Temporal Event Documents Classification
###### Abstract  
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. BiLSTM; representative spatio-temporal event; document classification +
Footnote †: journal: Medical  
and complex data of social media, extract useful knowledge, monitor current major issues, and predict the future. Existing studies that detect event sentences in text documents focus on accurately detecting sentences with 4W (who, what, when, and where) features based on statistical techniques. That is, all 4W-based events included in the document are detected. However, while there are events in the document that are important for identifying a particular event, there are also events that are not. Insignificant events rather hinder the accurate identification of events. Thus, it is important to find only key events that can represent the contents of a document, rather than all events included in a document. In our previous study, we developed a model to classify representative spatio-temporal documents using CNN [5]. The study used training data consisting only of temporal and spatial attributes, and presented only CNN as a deep learning model. In this study, 'what' and 'who' attributes were added by expanding to the spatio-temporal event perspective. Mislabeled data were corrected while adding attributes to the existing training data. We increased the number of training data from 7400 to 10,000. Among various events that exist in a document, we define an event that is important to the main content of a document as a'representative spatio-temporal event'. A document containing representative spatio-temporal events is defined as \"representative spatio-temporal event document (RSTEDoc)\". Recurrent neural networks are deep learning models suitable for sequential or variable-length data. LSTM is a model that can maintain long-term dependence between sequence components by solving the vanishing gradient problem that appears in recurrent neural networks. We present the BiLSTM model, which is attracting attention as a natural language processing model. We summarize our contributions as follows. * We defined the RSTEDoc in terms of the most important event among multiple events included in a document. Detailed definitions are given in Section 3.1. * We built 10,000 gold-standard training datasets for classifying RSTEDocs. Since there are no publicly open training data for classifying RSTEDocs yet, we hand-built training data based on large-volume news articles. * We developed a BiLSTM-based classifier for RSTEDocs and compared to traditional machine learning, using deep learning-based models (CNN and LSTM) as a baseline. * The BiLSTM-based model had the highest performance among other machine learning models with an F1 score of 0.631 and an accuracy of 0.835. The rest of our study is organized as follows. In Section 2, we look at related studies on spatio-temporal event detection and deep-learning-based document classification. In Section 3, we define the research problem, and the machine learning model used in this study is briefly described. In Section 4, we describe the data used in the experiment.