Detection of Disaster Situational Awareness Tweets Using Ensemble Learning
Nang La Min Thar · 2024
Nowadays, people have embraced social media for more complex purposes such as communicating for update and reliable news during a disaster. Social media has developed into an essential information channel in recent years that can be utilized to improve disaster response. Due to the high feature dimensionality, categorizing situational awareness tweets based on user posts is a challenging procedure. This system developed a framework for classifying environmental hazards tweets to detect they are situational awareness or not by using machine learning techniques. Three different datasets are used in this system. Feature Extraction of this work has exploited TF-IDF features, linguistic features and psychometric features. Both linguistic and psychometric features were extracted using Linguistic Inquiry Word Count (LIWC). Information Gain approach was used for feature selection. Random Forest, Naïve Bayes and XGBoost are used as based classifiers of ensemble model. This system used two ensemble learning techniques: hard voting and soft voting. The findings suggest that using ensemble learning (hard voting) produced better outcomes than using ensemble learning (soft voting).