Towards a validated model for affective classification of texts

Genereux, M. and Evans, R.P. (2006) Towards a validated model for affective classification of texts In: Sentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006), 17 - 21 July 2006, Sydney, Australia.

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Abstract

In this paper, we present the results of experiments aiming to validate a two-dimensional typology of affective states as a suitable basis for affective classification of texts. Using a corpus of English weblog posts, annotated for mood by their authors, we trained support vector machine binary classifiers to distinguish texts on the basis of their affiliation with one region of the space. We then report on experiments which go a step further, using four-class classifiers based on automated scoring of texts for each dimension of the typology. Our results indicate that it is possible to extend the standard binary sentiment analysis (positive/negative) approach to a two dimensional model (positive/negative; active/passive), and provide some evidence to support a more fine-grained classification along these two axes.

Item Type: Contribution to conference proceedings in the public domain ( Full Paper)
Additional Information: © 2006 Association for Computational Linguistics
Subjects: Q000 Languages and Literature - Linguistics and related subjects > Q100 Linguistics
G000 Computing and Mathematical Sciences > G700 Artificial Intelligence
Faculties: Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Natural Language Technology
Depositing User: Helen Webb
Date Deposited: 17 Nov 2007
Last Modified: 21 May 2014 11:01
URI: http://eprints.brighton.ac.uk/id/eprint/3186

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