An evaluation of text classification methods for literary study.
This article presents an empirical evaluation of text classification methods in literary domain. This study compared the performance of two popular algorithms, naïve Bayes and support vector machines (SVMs) in two literary text classification tasks: the eroticism classification of Dickinson's poems...
| Publicado en: | Literary & Linguistic Computing Vol. 23; no. 3; pp. 327 - 344 |
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| Formato: | Artículo |
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Oxford University Press / USA
Sep2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=35574713&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 35574713 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: Sep2008 vid: 23 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 35574713 10.1093/IIc/fqn015 ppf: 327 ppct: 17 formats: fmt: @attributes: type: P size: 349KB tig: atl: An evaluation of text classification methods for literary study. aug: au: Bei Yu affil: Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, USA su: Poetry (Literary form) Domain-specific programming languages Algorithms Vector analysis Literary discourse analysis Literary form Text processing (Computer science) Text files Computer network resources sug: subj: Poetry (Literary form) Domain-specific programming languages Algorithms Vector analysis Literary discourse analysis Literary form Text processing (Computer science) Text files Computer network resources ab: This article presents an empirical evaluation of text classification methods in literary domain. This study compared the performance of two popular algorithms, naïve Bayes and support vector machines (SVMs) in two literary text classification tasks: the eroticism classification of Dickinson's poems and the sentimentalism classification of chapters in early American novels. The algorithms were also combined with three text pre-processing tools, namely stemming, stopword removal, and statistical feature selection, to study the impact of these tools on the classifiers' performance in the literary setting. Existing studies outside the literary domain indicated that SVMs are generally better than naïve Bayes classifiers. However, in this study SVMs were not all winners. Both algorithms achieved high accuracy in sentimental chapter classification, but the naïve Bayes classifier outperformed the SVM classifier in erotic poem classification. Self-feature selection helped both algorithms improve their performance in both tasks. However, the two algorithms selected relevant features in different frequency ranges, and therefore captured different characteristics of the target classes. The evaluation results in this study also suggest that arbitrary featurereduction steps such as stemming and stopword removal should be taken very carefully. Some stopwords were highly discriminative features for Dickinson's erotic poem classification. In sentimental chapter classification, stemming undermined subsequent feature selection by aggressively conflating and neutralizing discriminative features. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Literary & Linguistic Computing holder: Oxford University Press / USA dt: @attributes: year: 2008 holdings: @attributes: islocal: N |
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