MOJ
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Mini Review Volume 4 Issue 3
Detecting emotional ambiguity in text
Jeffrey Jenkins
The Catholic University of America, Dept. of Electrical Engineering and Computer Science, USA
Received: August 29, 2019 | Published: May 25, 2020
Citation: Jenkins J. Detecting emotional ambiguity in text. MOJ App Bio Biomech. 2020;4(3):55?57. DOI: 10.15406/mojabb.2020.04.00134
Abstract
An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal topics which do not have a concrete emotional consensus.
Keywords: emotion, ambiguity, deep learning, natural language processing
©2020 Jenkins. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.
