Modeling Events And Affects In Social Media Stories

Stories play an important role in human perception of the world and therefore the computational analysis of narrative structure is a key area in natural language processing. The focus of this thesis is to develop and evaluate computational models for two main elements of the narrative structure: Events and Desires. Our work first aims to test a theory that proposes a linear structure of narratives and identifies different parts of a story based on their function. Unlike most of the previous work that use the news articles or other simpler and more conventional genres, we use a corpus of personal stories from social media that have a wider range of topical content and variations of discourse relations. We present an unsupervised method for modeling narrative events, focusing on specific event relations based on the Penn Discourse Treebank's definition of contingency. We use a weakly supervised approach to extract the key events from stories and create a topic-sorted corpus of personal narratives using a bootstrapping method. We additionally propose new evaluation methods for testing the contingent event pairs. Our results show that most of the relations we learn from blog stories are not found in the existing event collections. In our final contribution, we develop supervised methods for modeling the protagonist's goals and their outcome in personal narratives, as a sub-problem of modeling affects. Our studies show that both prior and post context are useful for modeling desire fulfillment. In addition, we show that exploiting narrative structure is helpful, both directly in terms of the utility of discourse relation features and indirectly by using a sequential model. We further examine our analysis of the human desires by identifying and studying the expressions of unfulfilled goals.