Enhancing disaster detection on social media with natural language processing.

In the contemporary globalized society, social media serves as a crucial component of our everyday routines. Among the various platforms, Twitter has emerged as a prominent micro-blogging and social networking site, enabling users to share news, information, and personal reflections. During times of emergencies or disasters, Twitter has proven to be an invaluable communication channel. The widespread usage of smartphones and tablets, individuals can promptly report emergencies in real-time, potentially saving numerous lives by alerting others to take necessary precautions. Recognizing the significance of Twitter in crisis situations, several organizations are actively engaged in programmatically analyzing tweets to identify and respond to disasters and emergencies. Such efforts can benefit millions of internet users by providing timely alerts during times of crisis. However, the main challenge lies in distinguishing between tweets that are directly related to a disaster and unrelated disaster. Unstructured nature of Twitter data is given, the application of Natural Language Processing (NLP) becomes essential for effectively classifying tweets as either "Related to Disaster" or "Not Related to Disaster". This research paper focuses on the development of a decision tree classifier model that leverages NLP techniques. The model's accuracy is evaluated by making predictions on a test set created from the original dataset. By employing this approach, we aim to address the crucial task of accurately identifying disaster-related tweets amidst the vast sea of Twitter data. [ABSTRACT FROM AUTHOR]