Dynamic windowing mechanism to combine sentiment and N-gram analysis in detecting events from social media

Social sensing is a new paradigm that inherits the main ideas of sensor networks and considers the users as new sensor types. For instance, by the time the users find out that an event has happened, they start to share the related posts and express their feelings through the social networks. Consequently, these networks are becoming a powerful news media in a wide range of topics. Existing event detection methods mostly focus on either the keyword burst or sentiment of posts, and ignore some natural aspects of social networks such as the dynamic rate of arriving posts. In this paper, we devised Dynamic Social Event Detection approach that exploits a new dynamic windowing method. Besides, we add a mechanism to combine the sentiment of posts with the keywords burst in the dynamic windows. The combination of sentiment analysis and the frequently used keywords enhances our approach to detect events with a different level of user engagement. To analyze the behavior of the devised approach, we use a wide range of metrics including histogram of window sizes, sentiment oscillations of posts, topic recall, keyword precision, and keyword recall on two benchmarked datasets. One of the significant outcomes of the devised method is the topic recall of 100% for FA Cup dataset.