Identifying and Analyzing Disaster-Related Tweet, Through Hashtag Monitoring Using Data Mining and NLP Techniques

Social networks provide more information on trends, ideas, and emotions during any such traumatic event as natural disasters. This paper proposes a model that monitors specific hashtag-related tweets, identifies the actual disaster tweet, and extracts meaningful data on disaster tweets, such as location and keywords. Government and organizations can use this data to support financial help, emergency evacuation, and volunteer. Monitoring Twitter and collecting data from many disaster periods, and analyze those data. This analysis data finds out where the Twitter users send the most catastrophic tweets and the most devastating event. An API developed from this data. This API served the disaster related location and corresponding disaster rate. A web app was developed based on the API. It detects the user’s location and displays the disaster statistics messages such as the disaster level related to the user’s area. Then users can receive this message via email or SMS by providing their phone numbers and emails. This work’s main objective is to identify the accurate disaster-related tweet by monitoring Twitter during the disaster and extracting meaningful data.