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JACIII Vol.23 No.3 pp. 512-518
doi: 10.20965/jaciii.2019.p0512
(2019)

Paper:

Emotion Prediction and Cause Analysis Considering Spatio-Temporal Distribution

Saki Kitaoka and Takashi Hasuike

Waseda University
3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan

Received:
September 28, 2018
Accepted:
December 25, 2018
Published:
May 20, 2019
Keywords:
emotion estimation, spatio-temporal distribution, Twitter-LDA, biterm topic model
Abstract
Emotion Prediction and Cause Analysis Considering Spatio-Temporal Distribution

Flow of our proposed method

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.

Cite this article as:
S. Kitaoka and T. Hasuike, “Emotion Prediction and Cause Analysis Considering Spatio-Temporal Distribution,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 512-518, 2019.
Data files:
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Last updated on Jul. 16, 2019