Publication
Title
Towards an empirically grounded framework for emotion analysis
Author
Abstract
The first step in training a system for automatic emotion detection consists of manual data annotation. Because there is no consensus on a standard emotion framework, we established a label set which is justified both theoretically and practically. Frequency and cluster analysis of 229 tweet annotations resulted in a label set containing the 5 emotions Love, Joy, Anger, Nervousness and Sadness. Our label set shows fair resemblance to Ekman's basic emotions, but due to our data-driven approach, our label set is much more grounded in the task (emotion detection) and the domain (Dutch tweets).
Language
English
Source (book)
HUSO 2019, the Fifth International Conference on Human and Social Analytics
Publication
IARIA , 2019
ISBN
978-1-61208-725-2
Volume/pages
p. 11-16
UAntwerpen
Publication type
Subject
External links
Record
Identifier
Creation 06.11.2023
Last edited 17.06.2024
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