Machine Learning Approaches to Sentiment Analytics
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.
Recommended Citation
Zhao, W., & Siau, K. L. (2017). Machine Learning Approaches to Sentiment Analytics. Proceedings of the 12th Midwest Association for Information Systems Conference (2017, Springfield, IL) Association for Information Systems (AIS).
Meeting Name
12th Midwest Association for Information Systems Conference, MWAIS 2017 (2017: May 18-19, Springfield, IL)
Department(s)
Business and Information Technology
Keywords and Phrases
Sentiment Analytics; Emotion classification; Machine Learning
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Association for Information Systems (AIS), All rights reserved.
Publication Date
19 May 2017
