Personalized Emotion Recognition Considering Situational Information and Time Variance of Emotion
Summary :
To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.11 pp.2409-2416
- Publication Date
- 2013/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.2409
- Type of Manuscript
- PAPER
- Category
- Human-computer Interaction
Authors
Yong-Soo SEOL
Hanyang University
Han-Woo KIM
Hanyang University
Keyword
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Yong-Soo SEOL, Han-Woo KIM, "Personalized Emotion Recognition Considering Situational Information and Time Variance of Emotion" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 11, pp. 2409-2416, November 2013, doi: 10.1587/transinf.E96.D.2409.
Abstract: To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2409/_p
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@ARTICLE{e96-d_11_2409,
author={Yong-Soo SEOL, Han-Woo KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Personalized Emotion Recognition Considering Situational Information and Time Variance of Emotion},
year={2013},
volume={E96-D},
number={11},
pages={2409-2416},
abstract={To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.},
keywords={},
doi={10.1587/transinf.E96.D.2409},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Personalized Emotion Recognition Considering Situational Information and Time Variance of Emotion
T2 - IEICE TRANSACTIONS on Information
SP - 2409
EP - 2416
AU - Yong-Soo SEOL
AU - Han-Woo KIM
PY - 2013
DO - 10.1587/transinf.E96.D.2409
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2013
AB - To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.
ER -
English
