抄録
This paper proposes an analytical model that clarifies the relationship between specific place and human emotions as well as the cause of the emotions using tweet data with location information. In addition, Twitter data with location information are analyzed to show the effectiveness of our proposed model. First, geotags are provided to collect Twitter data and increase the number of data for analysis. Second, training data with emotion labels based on the emotion expression dictionary are created and used, and supervised learning is done using fastText to obtain the emotion estimates. Finally, by using the result, topic extraction is performed to estimate the causes of the emotions. As a result, the transition of emotion in time and space as well as its cause is obtained.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 512-518 |
| ページ数 | 7 |
| ジャーナル | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| 巻 | 23 |
| 発行部数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2019 5 1 |
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ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence
これを引用
Emotion prediction and cause analysis considering spatio-temporal distribution. / Kitaoka, Saki; Hasuike, Takashi.
：: Journal of Advanced Computational Intelligence and Intelligent Informatics, 巻 23, 番号 3, 01.05.2019, p. 512-518.研究成果: Article
}
TY - JOUR
T1 - Emotion prediction and cause analysis considering spatio-temporal distribution
AU - Kitaoka, Saki
AU - Hasuike, Takashi
PY - 2019/5/1
Y1 - 2019/5/1
N2 - This paper proposes an analytical model that clarifies the relationship between specific place and human emotions as well as the cause of the emotions using tweet data with location information. In addition, Twitter data with location information are analyzed to show the effectiveness of our proposed model. First, geotags are provided to collect Twitter data and increase the number of data for analysis. Second, training data with emotion labels based on the emotion expression dictionary are created and used, and supervised learning is done using fastText to obtain the emotion estimates. Finally, by using the result, topic extraction is performed to estimate the causes of the emotions. As a result, the transition of emotion in time and space as well as its cause is obtained.
AB - This paper proposes an analytical model that clarifies the relationship between specific place and human emotions as well as the cause of the emotions using tweet data with location information. In addition, Twitter data with location information are analyzed to show the effectiveness of our proposed model. First, geotags are provided to collect Twitter data and increase the number of data for analysis. Second, training data with emotion labels based on the emotion expression dictionary are created and used, and supervised learning is done using fastText to obtain the emotion estimates. Finally, by using the result, topic extraction is performed to estimate the causes of the emotions. As a result, the transition of emotion in time and space as well as its cause is obtained.
KW - Biterm topic model
KW - Emotion estimation
KW - Spatio-temporal distribution
KW - Twitter-LDA
UR - http://www.scopus.com/inward/record.url?scp=85066237447&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066237447&partnerID=8YFLogxK
U2 - 10.20965/jaciii.2019.p0512
DO - 10.20965/jaciii.2019.p0512
M3 - Article
AN - SCOPUS:85066237447
VL - 23
SP - 512
EP - 518
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
SN - 1343-0130
IS - 3
ER -