| 英文摘要 | Along with the rapid development of social media, massive subjective remarks, which contain individual emotions, accumulate on the Web. Mining the implicit opinions as well as emotions from these textual remarks is crucial to enormous practical applications, such as public safety systems, business intelligent services and social monitoring and managements on the mass. As a result, opinion mining and sentiment analysis has become one of the central research topics of network-oriented social media analysis and mining domain. Currently, opinion mining and sentiment analysis research work can be categorized into mainly two branches: orientation extraction and emotion extraction. However, traditional approaches to orientation extraction have mainly focused on mining the polarities of opinions rather than the colorful emotion types; traditional approaches to emotion extraction requires great amount of annotated data even though they can output emotion types. Moreover, emotion theories, which identify the underlying cognitive structure and emotional dimensions that are key to generate emotions, have almost been totally ignored in previous work. Therefore, to facilitate the automatic extraction of emotions from textual data, in this thesis, we propose an emotion model based opinion mining method to automatically extract emotion types from text. The main contributions of our work are as follows. Informed by the mature cognitive structure of emotion model (abbreviated as OCC), this work has designed and implemented an OCC model based opinion mining method for extracting emotion types from text. To begin with, we first employ a statistical method to construct the emotion-dictionary based on the candidate sets collected by general semantic dictionary and several syntactic templates we design and a small amount of annotated data. We then refine the constructed emotion-dimension dictionary by filtering out emotional words which have conflicting semantics or orientations as well as non-emotional words. As the emotion-dimension dictionary is readily prepared, we can utilize OCC emotion model as a variety of mapping rules between emotional dimensions and emotion types, to generate the corresponding six basic emotion types in the text, that is, Joy, Distress, Hope, Fear, Pride, Shame. This thesis has improved the OCC model based opinion mining method by designing and implementing an OCC model based opinion mining algorithm which integrates Bootstrapping ... |
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