Affect analysis in context of characters in narratives

Authors:
Michal Ptaszynski;Hiroaki Dokoshi;Satoshi Oyama;Rafal Rzepka;Masahito Kurihara;Kenji Araki;Yoshio Momouchi
Affiliations:
Hokkai-Gakuen University, High-Tech Research Center, Minami 26, Nishi 11, Chuo-ku, Sapporo 064-0926, Japan;Hokkaido University, Graduate School of Information Science and Technology, Kita-ku Kita 14 Nishi 9, 060-0814 Sapporo, Japan;Hokkaido University, Graduate School of Information Science and Technology, Kita-ku Kita 14 Nishi 9, 060-0814 Sapporo, Japan;Hokkaido University, Graduate School of Information Science and Technology, Kita-ku Kita 14 Nishi 9, 060-0814 Sapporo, Japan;Hokkaido University, Graduate School of Information Science and Technology, Kita-ku Kita 14 Nishi 9, 060-0814 Sapporo, Japan;Hokkaido University, Graduate School of Information Science and Technology, Kita-ku Kita 14 Nishi 9, 060-0814 Sapporo, Japan;Hokkai-Gakuen University, Department of Electronics and Information Engineering, Faculty of Engineering, Minami 26, Nishi 11, Chuo-ku, Sapporo 064-0926, Japan
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

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.