An Ensemble Learning for Detecting Situational Awareness Tweets during Environmental Hazards

摘要

The shift to social media platforms like Twitter during environmental hazards and emergencies has expanded recently. Yet, the classification of situational awareness tweet based on people post is a complicated process due to the high dimensionality of features. In this empirical study, A framework using machine learning and Natural Language Processing techniques was developed for two-stage binary classification of Twitter data. The First stage consists of four models: Random Forest, Support Vector Machine, Naive Bayes and Decision Trees. Whereas, the second stage includes an ensemble learning approach. Text features - TFIDF (term frequency, inverse document frequency), psychometric, and linguistic - were analyzed as predictors of binary classification to categorize each tweet as situational relevant or irrelevant automatically. A manually built and labeled dataset of 4,000 tweets were analyzed for situational awareness of environmental health hazards in Barbados from water, mosquito-borne diseases, and sewage during the period 2014 - 2018. Based on the experiment, our model was able to achieve over 85% accuracy on classifying tweets that contribute to situational awareness. Furthermore, the results indicate that applying ensemble learning in the second stage showed superior results compared to the combined features-based classification models.