A comparative analysis of detection mechanisms for emotion detection

Balakrishnan, Vimala, Martin, Marian Cynthia, Kaur, Wandeep and Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945 2019. A comparative analysis of detection mechanisms for emotion detection. Presented at: International Conference Computer Science and Engineering (IC2SE 2019), Padang, Indonesia, 26-27 April 2019. Journal of Physics: Conference Series. , vol.1339 IOP Publishing: Conference Series, 012016.

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Abstract

This paper compared the performance of emotion detection mechanisms using dataset crawled from Facebook diabetes support group pages. To be specific, string-based Multinomial Naïve Bayes algorithm, NRC Emotion Lexicon (Emolex) and Indico API were used to detect five emotions present in 2475 Facebook posts, namely, fear, joy, sad, anger and surprise. Both accuracy and F-score measures were used to assess the effectiveness of the algorithms in detecting the emotions. Findings indicate string-based Multinomial Naïve Bayes to outperform both Emolex (i.e. 82% vs. 78%) and Indico API (i.e. 82% vs. 50%). Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community. Implications of the findings and emotions detected are further discussed in this paper

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	In Press
Schools:	Computer Science & Informatics
Publisher:	IOP Publishing: Conference Series
ISSN:	1742-6588
Date of First Compliant Deposit:	27 May 2020
Last Modified:	07 Nov 2022 10:20
URI:	https://orca.cardiff.ac.uk/id/eprint/131941

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