Comparing hierarchical approaches to enhance supervised emotive text classification

Williams, Lowri, Anthi, Eirini and Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X 2024. Comparing hierarchical approaches to enhance supervised emotive text classification. Big Data and Cognitive Computing 8 (4) 10.3390/bdcc8040038

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Abstract

The performance of emotive text classification using affective hierarchical schemes (e.g. WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation into how methods used in hierarchical classification problems in other domains, which extend traditional evaluation metrics to consider the characteristics of the hierarchical classification scheme can be applied and subsequently improve the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all methods improved the emotion classification. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
ISSN:	2504-2289
Funders:	ESRC
Date of First Compliant Deposit:	2 April 2024
Date of Acceptance:	26 March 2024
Last Modified:	17 Apr 2024 16:20
URI:	https://orca.cardiff.ac.uk/id/eprint/167555

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