Aconcagua: A Novel Spatiotemporal Emotion Change Analysis Framework

In this paper, we introduce Aconcagua, a novel spatio-temporal emotion change analysis framework. Our current research uses Twitter tweets as the knowledge source for emotion analysis. The inputs for the emotion mapping and change analysis system, we are currently developing, are the location and time of the tweets and their corresponding emotion assessment score falling in the range [-1, +1], with +1 representing a very positive emotion and -1 representing a very negative emotion. We start by identifying spatial clusters that capture positive and negative emotion regions for batches of the dataset with each batch corresponding to a specific time interval, e.g. a single day. These obtained spatial clusters and their statistical summaries are then used as the input for Aconcagua which monitors change of emotions with respect to a set of unary and binary change predicates that are evaluated with respect to the set of spatial clusters; as the result of this process an emotion change graph is obtained whose nodes are spatial clusters and whose edges capture different types of temporal relationships between spatial clusters. An implementation of the change monitoring process is discussed which operates on top of a relational database and uses SQL queries to specify change predicates. To obtain more aggregated change summaries and ultimately change stories, the change graph further must be mined and summarized based on what aspects of change the analyst is interested in. To support such capabilities, our approach supports several types of change analysis templates called story types. We demo our approach using tweets collected in the state of New York in June 2014.