SentimentMapper: A framework for mapping of sentiments towards disaster response using social media data

Abstract

Social networking platforms have been generating a massive amount of data in real-time that can be analysed and used to support government and relief organizations in preparing quick and effective action plans for disaster response. Effective disaster response requires a broad understanding of disaster situations, such as the emergency necessities of the people, their sentiments towards emergency needs, and the geographical distribution of their requirements and opinions. However, in literature, many studies exist that estimate the emotions and sentiments of the people during a disaster; they are inept in identifying and mapping the public sentiments toward emergency needs. This paper proposes a framework called SentimentMapper. This framework quickly maps the sentiments of people toward emergency needs using social media data to plan for effective disaster response. In order to perform an automatic analysis of sentiments using Twitter (re-branded to X since July 2023) data, we introduce a BERT Convolutional Neural Network (BCNN). BCNN performs the sentiment analysis of the collected data from the disaster-affected people regarding essential needs like food, shelter, medical emergency, and rescue during different disasters. Next, we present a tweet-text independent approach to detect the location of the tweets posted on Twitter and discover the impacts in different areas due to any disaster event. Furthermore, we also study the variations in public attitudes about the essential needs during identical or different disasters. As a case study, the proposed framework has been used on the dataset collected from Twitter during the Assam flood 2021 in India and validated with the corresponding survey reports published by the government agency. The detailed results of the analytics in the proposed framework and its validation with the case study data confirm that it is capable of providing credible situational information quickly required for the disaster responses.