Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2017
Abstract
One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces a higher accuracy in classifying emotions. We also observe that training sample sizes have different impact on the two approaches.
Keywords
Sentiment Analytics, Emotion classification, Machine Learning
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Information Systems and Management
Areas of Excellence
Digital transformation
Publication
Proceedings of the Twelve Annual Midwest Association for Information Systems Conference (MWAIS 2017), Springfield, Illinois, May 18-19
First Page
1
Last Page
5
Publisher
MWAIS
City or Country
Springfield, Illinois
Citation
ZHAO, W. and SIAU, Keng.
Machine learning approaches to sentiment analytics. (2017). Proceedings of the Twelve Annual Midwest Association for Information Systems Conference (MWAIS 2017), Springfield, Illinois, May 18-19. 1-5.
Available at: https://ink.library.smu.edu.sg/sis_research/9410
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.