Call for Papers
About the Journal
Editorial Board
Publication Ethics
Instructions for Authors
Announcements
Current Issue
Back Issues
Search for Articles
Categories
Back Issues
 

JCSE, vol. 10, no. 4, pp.103-110, December, 2016

DOI: http://dx.doi.org/10.5626/JCSE.2016.10.4.103

Use of Word Clustering to Improve Emotion Recognition from Short Text

Shuai Yuan, Huan Huang, and Linjing Wu
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China School of Educational Information Technology, Central China Normal University, Wuhan, China

Abstract: Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

Keyword: Emotion recognition; Affective computing; Word clustering

Full Paper:   1050 Downloads, 1005 View

 
 
ⓒ Copyright 2010 KIISE – All Rights Reserved.    
Korean Institute of Information Scientists and Engineers (KIISE)   #401 Meorijae Bldg., 984-1 Bangbae 3-dong, Seo-cho-gu, Seoul 137-849, Korea
Phone: +82-2-588-9240    Fax: +82-2-521-1352    Homepage: http://jcse.kiise.org    Email: office@kiise.org