Abstract
A rare event, such as a natural disaster, does not occur frequently, but may cause catastrophic impacts on human beings and their living environment once it happens. Social media provides people an immediate and convenient way to share their opinions. Researchers can thus use it for investigating people's actions, feelings, and attitudes during multiple rare events. By using social media data, many studies try to build the connection between rare events in a real world and people's responses, including feelings, attitudes, and behaviors, in a virtual world. In this article, we propose a feature extraction and text classification approach to Twitter text data related to a rare event, e.g., Hurricane Sandy. First, a novel feature extraction method is proposed to mine useful features from each text message. Next, a fuzzy logic-based classification method is proposed to distinguish event-related and unrelated messages. Finally, our proposed method is compared with the existing keyword search one that is widely used in analyzing the evolution of a rare event. The result reveals that the proposed approach is suitable to identify the rare event-related and unrelated text messages.