Geo-Spatial Multimedia Sentiment Analysis in Disasters

Sentiment analysis of disaster-related posts in social media can contribute to the situation awareness and better understanding of the dynamics of disaster events by identifying the polarity of sentiments expressed by the public. However, Even though many sentiment analysis techniques have been developed and available, there are still limitations in reliably using sentiment analysis since there is no dominantly accepted technique in disasters. Taking advantage of existing state-of-the-art sentiment classifiers, this paper proposes a novel framework for geo-spatial sentiment analysis of disaster-related social media data objects. Our framework addresses three types of challenges: the inaccuracy and discrepancy associated with various text and image sentiment classifiers, the geo-sentiment discrepancy among data objects in a local geographical area, and observing diverse sentiments from multimedia data objects (i.e., text and image). The extracted sentiments are aggregated geographically for the purpose of extracting more accurate local regional insights. For the evaluation of the framework, we explored Twitter and Flickr datasets at the time of Hurricane Sandy and Napa Earthquake and showed how our approach can provide a better understanding of disaster events.

I. Introduction

Online social media (e.g., Twitter, Flickr, Instagram, and Facebook) have attracted millions of users to communicate and share thoughts and feelings about their daily lives. Especially, people can share geo-tagged data objects (e.g., images or texts) anytime and anywhere, which potentially provides a valuable source of knowledge about the physical environment and social phenomena. Many studies have utilized social media data to investigate human behaviors and public characteristics (e.g., demographic and urban characteristics [16] [8], customer opinions about products [14] [19], political views [17], and public health information [18]). Furthermore, social media has been used as active sensors during emergency events [15] [20] such as disasters (e.g., flood, earthquake, and hurricane). In particular, the Federal Emergency Management Agency (FEMA) identifies social media as an essential component of future disaster management [35].