|Title||Recognition of Fine-Grained Emotions from Text: An Approach Based on the Compositionality Principle|
|Publication Type||Book Chapter|
|Year of Publication||2010|
|Authors||Neviarouskaya, A, Prendinger, H, Ishizuka, M|
|Editor||Howlett, RJ, Jain, LC, Nishida, T, Jain, LC, Faucher, C|
|Book Title||Modeling Machine Emotions for Realizing Intelligence|
|Series Title||Smart Innovation, Systems and Technologies|
This chapter addresses the tasks of recognition, interpretation and visualization of affect communicated through text messaging in virtual communication environments. In order to facilitate sensitive and expressive communication in such environments, we introduced a novel syntactic rule-based approach to affect recognition from text. Our Affect Analysis Model follows the compositionality principle, according to which emotional meaning of a sentence is determined by composing parts that correspond to lexical units or other linguistic constituent types governed by the rules of aggregation, propagation, domination, neutralization, and intensification, at various grammatical levels. The proposed rule-based approach processes each sentence in sequential stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences. Affect in text is classified into nine emotion categories (or neutral), and, additionally, information that indicates social communicative behaviour is identified. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize affective information in text from an existing corpus of informal online conversations. The applications of the developed Affect Analysis Model in Instant Messaging system (AffectIM) and in Second Life (EmoHeart, iFeel_IM!) are described in the chapter.