Download mcp9117/5/2023 We show how users' sentiments change according not only to users' locations, but also based on the distance from the disaster. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. We highlight various applications and opportunities of SM multimodal data, latest advancements, current challenges, and future directions for the crisis informatics and other related research fields. These challenges include near-real-time information processing, information overload, information extraction, summar-ization, and verification of both textual and visual content. However, several challenges prevent these organizations from using SM data for response efforts. Several studies have shown the utility of SM information for disaster response and management, which encouraged humanitarian organizations to start incorporating SM data sources into their workflows. Information on SM comes in many forms, such as textual messages, images, and videos. People increasingly use Social Media (SM) platforms such as Twitter and Facebook during disasters and emergencies to post situational updates including reports of injured or dead people, infrastructure damage, requests of urgent needs, and the like.
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