Emotion Prediction and Cause Analysis Considering Spatio-Temporal Distribution

Saki Kitaoka; Takashi Hasuike

doi:10.20965/jaciii.2019.p0512

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JACIII Vol.23 No.3 pp. 512-518

doi: 10.20965/jaciii.2019.p0512

(2019)

Paper:

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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

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.

Flow of our proposed method

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:

References

[1] P. Georgiev, A. Noulas, and C. Mascolo, “Where businesses thrive: Predicting the impact of the Olympic games on local retailers through location-based services data,” Proc. of the 8th Int. AAAI Conf. on Weblogs and Social Media, pp. 151-160, 2014.
[2] D. Hristova, D. Liben-Nowell, A. Noulas, and C. Mascolo, “If You’ve Got the Money, I’ve Got the Time: Spatio-Temporal Footprints of Spending at Sports Events on Foursquare,” The Workshops of the 10th Int. AAAI Conf. on Web and Social Media CityLab: Technical Report WS-16-16, pp. 15-19, 2016.
[3] T. H. Silva, P. O. S. Vaz de Melo, J. Almeida, M. Musolesi, and A. Loureiro, “You Are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare,” Proc. of the 8th Int. AAAI Conf. on Weblogs and Social Media, pp. 466-475, 2014.
[4] J. Han and H. Yamana, “A study on individual mobility patterns based on individuals’ familiarity to visited areas,” Int. J. of Pervasive Computing and Communications, Vol.12, No.1, pp. 23-48, 2016.
[5] T. N. Maeda, M. Yoshida, F. Toriumi, and H. Ohashi, “Decision Tree Analysis of Tourists’ Preferences Regarding Tourist Attractions Using Geotag Data from Social Media,” Proc. of the 2nd Int. Conf. on IoT in Urban Space, pp. 61-64, 2016.
[6] A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Stanford University CS224N Project Report, 2009.
[7] H. Takamura, T. Inui, and M. Okumura, “Extracting semantic orientations of words using spin model,” Proc. of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 133-140, 2005.
[8] I. Keshi, Y. Suzuki, K. Yoshino, N. Graham, K. Ohara, T. Mukai, and S. Nakamura, “Reputation Information Extraction from Twitter Using a Word Semantic Vector Dictionary,” J. of Institute of Electronics, Information and Communication Engineers, Vol.J100-D, No.4, pp. 530-543, 2017 (in Japanese).
[9] S. Watanabe, K. Arasawa, M. Eida, and S. Hattori, “Spatio-Temporal Emotion Estimation for Automatic Map Generation of Emotion Distributions,” IEICE Technical Report IN2016-94, Vol.116, No.400, pp. 55-60, 2016 (in Japanese).
[10] K. Taguchi, K. Misue, and J. Tanaka, “Visualization for Spatiotemporal Distribution of People’s Rich Emotions,” IPSJ SIG Technical Report, Vol.2014-EC-31, No.36, pp. 1-8, 2014 (in Japanese).
[11] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Proc. of Advances in Neural Information Processing Systems 26, pp. 1-9, 2013.
[12] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint, arXiv:1301.3781, 2013.
[13] A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of tricks for efficient text classification,” arXiv preprint, arXiv:1607.01759, 2016.
[14] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. of Machine Learning Research, Vol.3, pp. 993-1022, 2003.
[15] W. X. Zhao, J. Jiang, J. Weng, J. He, E. P. Lim, H. Yan, and X. Li, “Comparing twitter and traditional media using topic models,” P. Clough et al. (Eds.), Advances in Information Retrieval, ECIR 2011, Lecture Notes in Computer Science, Vol.6611, pp. 338-349, Springer, 2011.
[16] X. Yan, J. Guo, Y. Lan, and X. Cheng, “A biterm topic model for short texts,” Proc. of the 22nd Int. Conf. on World Wide Web, pp. 1445-1456, 2013.
[17] R. Plutchik, “A general psychoevolutionary theory of emotion,” Theories of Emotion, pp. 3-33, 1980.
[18] H. N. Gardiner, R. C. Metcalf, and J. G. Beebe-Center, “Feeling and emotion: A history of theories,” American Book Company, 1937.
[19] A. Nakamura (Eds.), “Emotional Expression Dictionary,” Tokyodo Publisher, 1993 (in Japanese).

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] P. Georgiev, A. Noulas, and C. Mascolo, “Where businesses thrive: Predicting the impact of the Olympic games on local retailers through location-based services data,” Proc. of the 8th Int. AAAI Conf. on Weblogs and Social Media, pp. 151-160, 2014.

[2] [2] D. Hristova, D. Liben-Nowell, A. Noulas, and C. Mascolo, “If You’ve Got the Money, I’ve Got the Time: Spatio-Temporal Footprints of Spending at Sports Events on Foursquare,” The Workshops of the 10th Int. AAAI Conf. on Web and Social Media CityLab: Technical Report WS-16-16, pp. 15-19, 2016.

[3] [3] T. H. Silva, P. O. S. Vaz de Melo, J. Almeida, M. Musolesi, and A. Loureiro, “You Are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare,” Proc. of the 8th Int. AAAI Conf. on Weblogs and Social Media, pp. 466-475, 2014.

[4] [4] J. Han and H. Yamana, “A study on individual mobility patterns based on individuals’ familiarity to visited areas,” Int. J. of Pervasive Computing and Communications, Vol.12, No.1, pp. 23-48, 2016.

[5] [5] T. N. Maeda, M. Yoshida, F. Toriumi, and H. Ohashi, “Decision Tree Analysis of Tourists’ Preferences Regarding Tourist Attractions Using Geotag Data from Social Media,” Proc. of the 2nd Int. Conf. on IoT in Urban Space, pp. 61-64, 2016.

[6] [6] A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Stanford University CS224N Project Report, 2009.

[7] [7] H. Takamura, T. Inui, and M. Okumura, “Extracting semantic orientations of words using spin model,” Proc. of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 133-140, 2005.

[8] [8] I. Keshi, Y. Suzuki, K. Yoshino, N. Graham, K. Ohara, T. Mukai, and S. Nakamura, “Reputation Information Extraction from Twitter Using a Word Semantic Vector Dictionary,” J. of Institute of Electronics, Information and Communication Engineers, Vol.J100-D, No.4, pp. 530-543, 2017 (in Japanese).

[9] [9] S. Watanabe, K. Arasawa, M. Eida, and S. Hattori, “Spatio-Temporal Emotion Estimation for Automatic Map Generation of Emotion Distributions,” IEICE Technical Report IN2016-94, Vol.116, No.400, pp. 55-60, 2016 (in Japanese).

[10] [10] K. Taguchi, K. Misue, and J. Tanaka, “Visualization for Spatiotemporal Distribution of People’s Rich Emotions,” IPSJ SIG Technical Report, Vol.2014-EC-31, No.36, pp. 1-8, 2014 (in Japanese).

[11] [11] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Proc. of Advances in Neural Information Processing Systems 26, pp. 1-9, 2013.

[12] [12] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint, arXiv:1301.3781, 2013.

[13] [13] A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of tricks for efficient text classification,” arXiv preprint, arXiv:1607.01759, 2016.

[14] [14] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. of Machine Learning Research, Vol.3, pp. 993-1022, 2003.

[15] [15] W. X. Zhao, J. Jiang, J. Weng, J. He, E. P. Lim, H. Yan, and X. Li, “Comparing twitter and traditional media using topic models,” P. Clough et al. (Eds.), Advances in Information Retrieval, ECIR 2011, Lecture Notes in Computer Science, Vol.6611, pp. 338-349, Springer, 2011.

[16] [16] X. Yan, J. Guo, Y. Lan, and X. Cheng, “A biterm topic model for short texts,” Proc. of the 22nd Int. Conf. on World Wide Web, pp. 1445-1456, 2013.

[17] [17] R. Plutchik, “A general psychoevolutionary theory of emotion,” Theories of Emotion, pp. 3-33, 1980.

[18] [18] H. N. Gardiner, R. C. Metcalf, and J. G. Beebe-Center, “Feeling and emotion: A history of theories,” American Book Company, 1937.

[19] [19] A. Nakamura (Eds.), “Emotional Expression Dictionary,” Tokyodo Publisher, 1993 (in Japanese).