An Evaluation of Text Classification Methods for Literary Study

Some of our conclusions are consistent with what are obtained in topic classification, such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this area---SVM and naive Bayes select top features in different frequency ranges; stemming might harm feature selection methods. These experiment results provide new insights to the relation between classification methods, feature engineering options and non-topic document properties. They also provide guidance for classification method selection in literary text classification applications.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Library and Information Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Yu, Bei

Contributors dc:contributor

• Linda Smith

• Literature, American

Language dc:language

eng

Identifier

(MiAaPQ)AAI3250350

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81543