Title

An Experimental Comparison of Two Machine Learning Approaches for Emotion Classification

Abstract

Correctly identifying an emotion has always been challenging for humans, not to mention machines! In this research, we use machine learning to classify human emotion. Emotional differences between genders are well documented in fields like psychology. We hypothesize that genders will impact the accuracy of classifying emotion with machine learning. Two different machine learning approaches were tested in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, the genders were separated and two separate machines were used to learn the emotions of the two genders. Our preliminary results show that the approach where the genders were separated produces higher accuracy in classifying emotion.

Meeting Name

23rd Americas Conference on Information Systems, AMCIS 2017 (2017: Aug. 10-12, Boston, MA)

Department(s)

Business and Information Technology

Comments

Emergent Research Forum Paper

Keywords and Phrases

Emotion Classification; Facial Expression; Sexes; Machine Learning

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Association for Information Systems (AIS), All rights reserved.

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