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Affect Analysis Model: novel rule-based approach to affect sensing from text

Published online by Cambridge University Press:  16 September 2010

ALENA NEVIAROUSKAYA ,
HELMUT PRENDINGER  and
MITSURU ISHIZUKA
ALENA NEVIAROUSKAYA
Affiliation:
Department of Information and Communication Engineering, University of Tokyo, Engineering Building 2, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan e-mails: lena@mi.ci.i.u-tokyo.ac.jp, ishizuka@i.u-tokyo.ac.jp
HELMUT PRENDINGER
Affiliation:
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan e-mail: helmut@nii.ac.jp
MITSURU ISHIZUKA
Affiliation:
Department of Information and Communication Engineering, University of Tokyo, Engineering Building 2, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan e-mails: lena@mi.ci.i.u-tokyo.ac.jp, ishizuka@i.u-tokyo.ac.jp
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Abstract

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in 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). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).

Type
Papers
Information
Natural Language Engineering , Volume 17 , Issue 1 , January 2011 , pp. 95 - 135
Copyright
Copyright © Cambridge University Press 2010

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