Affect analysis in context of characters in narratives

This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements.

► We perform research in character/person-based textual affect analysis of narratives.
► We propose a method for subject extraction from sentence based on anaphora.
► We compare two methods for affect analysis, first using WordNet, second using ML-Ask.
► The evaluation showed significant improvement with the use of ML-Ask over WordNet.