The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the finegrained level (14 labels) was 62%, on the middle level (7 labels) – 71%, and on the top level (3 labels) – 88%. 1 Introduction and Related Work

The automatic analysis and classification of text using fine-grained attitude labels is the main task we address in our research. The developed

Abstract. In this paper, we describe lexical rule-based approach to affect sensing from text, and application of the developed Affect Analysis Model in 3D virtual world Second Life. To enrich user experience in virtual environment, to automate emotional behaviour of avatar and to avoid thus manual control by user, we developed EmoHeart object that, driven by the result of Affect Analysis Model, triggers animations of avatar facial expressions and visualizes emotion by the heart-shaped textures.