A Linguistic Approach Towards Automatic Acquisition and Classification of Common Chinese Affective Events

Affective events are events that are typically associated with a positive or negative emotional state. For example, get food is a desirable event, while suffer from asthma is an undesirable event. Identifying affective events and their polarities is essential for many artificial intelligence (AI) tasks. Since sentiments toward an affective event is often implicit, affective event acquisition and classification have always been two great challenges. Encounter events are a special type of affective events. The goal of this article is to automatically acquire and classify stereotypically positive and negative encounter events in Chinese. First of all, we collect a comprehensive list of Chinese encounter event indicators based on the literature related to Chinese encounter events. Then, we automatically extract candidate encounter events from a text corpus using these indicators as keywords. In order to acquire high-quality encounter events, we design an event filtering scheme and generate a novel stopword list. Finally, we classify these obtained encounter events exploiting the indicators. Experimental results show that the proposed approaches to acquire and classify Chinese encounter events perform promisingly well. One key advantage of the method is that no labelling and training of data is required. The automatically constructed common affective event knowledge base contains more than 38,000 encounter events annotated with affective polarity labels (negative or positive), which is currently the only resource of common affective events in Chinese language. Related resources including encounter event indicators and encounter events will be released after the publication of this article.