"Sentiment analysis of calls and tweets recorded in a natural disaster emergency helpline to predict seriousness and optimize resorurce distribution"
Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand
Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets.
We aim to use various machine learning models and apis to train on the twitter post datasets to derive meaningful information to act upon and derive decisions like resource management and emergency handling.