An Analytical Approach for Affect Sensing from Text

Studying the relationship between natural language and affective information as well as assessing the underpinned affective meaning of natural language are becoming crucial for improving human computer interaction. The area of such interactive applications is numerous and varied, ranging from categorizing newsgroup flame and augmenting search engine responses to analysis of public opinion trends towards a particular fact or entity and customer feedback. Text is not only an important medium to describe facts and events, but also to effectively communicate information about the writer’s positive or negative sentiment underlying an opinion, or to express an affective or emotional state, such as happy, fearful, surprised, and so on. We consider sentiment assessment and emotion sensing from text as two different problems. Classifying the tone of the communication as generally positive or negative is considered as the task of sentiment assessment and recognition of particular emotion(s) being expressed is the task of emotion sensing. Therefore, the thesis first presents an analytical approach to sentiment assessment, i.e., the recognition of negative or positive valence of a sentence and then explains how a wellfounded emotion model has been implemented for recognition of emotions. For the purpose of sentiment assessment from text, we perform semantic dependency analysis on the semantic verb frame(s) of each sentence, and then apply a set of rules to each dependency relation to calculate the contextual valence of the words used in the sentence. By employing a domain-independent, rule-based approach our system is able to automatically identify sentence-level sentiment. A linguistic tool called ‘SenseNet’ has been developed to recognize sentiments in text, and to visualize the detected sentiments. We conducted several experiments with a variety of datasets containing data from different domains. The obtained results indicate significant performance gains over existing state-of-the-art approaches. Emotions expressed in natural language are very often expressed in subtle and complex ways, presenting challenges which may not be easily addressed by simple text categorization approaches such as ‘n-gram’ or ‘keyword identification’approaches. Numerous approaches have already been employed to “sense” affective information from text; but none of those ever employed the OCC emotion model – an influential theory of the cognitive and appraisal structure of emotion. The OCC model derives twenty-two emotion types and two cognitive states as consequences of several cognitive variables. This thesis therefore describes how to relate cognitive variables of the emotion model to linguistic components in text, in order to achieve emotion recognition for a much larger set of emotions than handled in comparable approaches. In particular, we provide tailored rules for textural emotion recognition, which are inspired by the rules of the OCC emotion model. Hereby, we clarify how text components can be mapped to specific values of the cognitive variables of the emotion model. The resulting linguistics-based rule set for the OCC emotion types and cognitive states allow us to determine a broad class of emotions conveyed by text.報告番号: 甲23944 ; 学位授与年月日: 2008-03-24 ; 学位の種別: 課程博士 ; 学位の種類: 博士(情報理工学) ; 学位記番号: 博情第189号 ; 研究科・専攻: 情報理工学系研究科電子情報学専