Towards a validated model for affective classification of texts

M. Genereux, Roger Evans

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNResearchpeer-review

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.
Original languageEnglish
Title of host publicationSentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006)
Publication statusPublished - 22 Jul 2006
EventSentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006) - Sydney, Australia
Duration: 22 Jul 2006 → …

Workshop

WorkshopSentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006)
Period22/07/06 → …

Fingerprint

Classifiers
Support vector machines
Experiments

Bibliographical note

© 2006 Association for Computational Linguistics

Cite this

Genereux, M., & Evans, R. (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)
Genereux, M. ; Evans, Roger. / Towards a validated model for affective classification of texts. Sentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006). 2006.
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title = "Towards a validated model for affective classification of texts",
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.",
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Genereux, M & Evans, R 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). Sentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006), 22/07/06.

Towards a validated model for affective classification of texts. / Genereux, M.; Evans, Roger.

Sentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006). 2006.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNResearchpeer-review

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T1 - Towards a validated model for affective classification of texts

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AU - Evans, Roger

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N2 - 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.

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M3 - Conference contribution with ISSN or ISBN

BT - Sentiment and Subjectivity in Text, Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2006)

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Genereux M, Evans R. 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). 2006