Conference Proceedings

Textual emotion classification: An interoperability study on cross-genre data sets

B Ofoghi, K Verspoor

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer | Published : 2017

Abstract

© Springer International Publishing AG 2017. This paper describes the application and analysis of a previously developed textual emotion classification system (READ-BioMed-EC) on a different data set in the same language with different textual properties. The classifier makes use of a number of lexicon-based and text-based features. The data set originally used to develop this classifier consisted of English-language Twitter microblogs with mentions of Ebola disease. The data was manually labelled with one of six emotion classes, plus sarcasm, news-related, or neutral. In this new work, we applied the READ-BioMed-EC emotion classifier without retraining to an independently collected set of W..

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