Abstract
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 Web blog posts, also annotated with emotion classes, to understand how well the Twitter-trained disease-focused emotion classifier might generalise to an entirely different collection of open-domain sentences. The results of our study show that direct cross-genre application of the classifier does not achieve meaningful results, but when re-trained on the open-domain data set, the READ-BioMed-EC system outperforms the previously published results. The study has implications for cross-genre applicability of emotion classifiers, demonstrating that emotion is expressed differently in different text types.
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Acknowledgments
We thank Saima Aman and Stan Szpakowicz for sharing their Web blog data set with us for the purpose of this study.
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Ofoghi, B., Verspoor, K. (2017). Textual Emotion Classification: An Interoperability Study on Cross-Genre Data Sets. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_21
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