Natural Disaster on Twitter: Role of Feature Extraction Method of Word2Vec and Lexicon Based for Determining Direct Eyewitness

Abstract

Researchers have collected Twitter data to study a wide range of topics, one of which is a natural disaster. A social network sensor was developed in existing research to filter natural disaster information from direct eyewitnesses, none eyewitnesses, and non-natural disaster information. It can be used as a tool for early warning or monitoring when natural disasters occur. The main component of the social network sensor is the text tweet classification. Similar to text classification research in general, the challenge is the feature extraction method to convert Twitter text into structured data. The strategy commonly used is vector space representation. However, it has the potential to produce high dimension data. This research focuses on the feature extraction method to resolve high dimension data issues. We propose a hybrid approach of word2vec-based and lexicon-based feature extraction to produce new features. The Experiment result shows that the proposed method has fewer features and improves classification performance with an average AUC value of 0.84, and the number of features is 150. The value is obtained by using only the word2vec-based method. In the end, this research shows that lexicon-based did not influence the improvement in the performance of social network sensor predictions in natural disasters. HIGHLIGHTS Implementation of text classification is generally only used to perform sentiment analysis, it is still rare to use it to perform text classification for use in determining direct eyewitnesses in cases of natural disasters One of the common problems in text mining research is the extracted features from the vector space representation method generate high dimension data A hybrid approach of word2vec-based and lexicon-based feature extraction experiment was conducted in order to find a method that can generate new features with low dimensions and also improve the classification performance GRAPHICAL ABSTRACT