Twitter and Sentiment Analysis for Wildfire Heat Mapping

ABSTRACT Nowadays, automated intelligent systems play an increasingly vital role in aiding decision‐making processes across various fields. Firefighting represents a crucial area where accurate information gathering is paramount for efficient resource allocation. Social media platforms as Twitter (or X) have emerged as valuable sources of real‐time data, often referred to as ‘citizen science’, offering additional insights alongside traditional data sources. In this work, we introduce a novel pipeline that leverages Natural Language Processing (NLP) techniques and Twitter data, utilising transformer models to identify and monitor wildfire incidents. Expanding on this approach, we incorporate sentiment analysis to provide deeper insights into public perceptions and emotions related to fire events. Additionally, we present visual representations of geographic data through heat mapping, potentially aiding firefighters in making informed decisions. By integrating advanced NLP techniques with social media data, our approach presents a promising strategy for enhancing wildfire management efforts.