Time Series Sentiment Analysis of Natural Hazard Relief Operations Using Social Media Platform for Efficient Resource Management

The ease of access to the internet has sparked a worldwide interest in social media in recent years. This paper present a machine learning based time series analysis for post-disaster relief efforts using social media information. The situational information is gathered using Twitter from the victims and resource providers to assist government, NGOs, and off-site aid providers. For the case study, public dataset from Nepal, Italy earthquakes, COVID-19 dataset along with originally collected Twitter dataset has been considered. Vectorization has been performed using TF-IDF for machine learning model and word embeddings for the deep learning model. The performance of the extreme gradient boosting (xgboost) model is relatively superior than other techniques with 10-fold training mean accuracy of 87.17 %. Justification of the results and transparency of machine learning model is described using Explainable Artificial Intelligence (XAI) method. The experiments suggest the possibility of automation for the time series analysis for optimal relief operation management to serve the victims in the most efficient way and to control legal and administration implications