Emotion Sensing for Internet Chatting: A Web Mining Approach for Affective Categorization of Events

This study proposes an emotion detection engine for real time Internet chatting applications. We adopt a Web scale text mining approach that automates the categorization of affection state of daily events. We first accumulated a huge collection of real-life entities from Web that would participate in events with a user in the chatting room. Based on the common actions between each entity and the type of the user in a chatting room session, such as boy, girl, old man and so on, each collected entity was automatically classified into different affective categories such as pleasant, provoking, grievous, and scary. During a chatting session, each sentence is first parsed using semantic roles labeling techniques to retrieve the verb and object of the event embedded in the sentence. Based on a set of manually authored emotion generation rule, the system then assigns the emotion based on the verb and the affective categories of the object. Primitive evaluations show that the precision rate of the emotion detection engine is rather satisfactory for applications that distinguish emotions of Happiness, Sadness, Anger, and Fear.