Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms

Yi Jie Huang; Chu Hsien Su; Yi Chun Chang; Tseng Hsin Ting; Tzu Yuan Fu; Rou Min Wang; Hong Jie Dai; Yung Chun Chang; Jitendra Jonnagaddala; Wen Lian Hsu

Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms

Yi Jie Huang, Chu Hsien Su, Yi Chun Chang, Tseng Hsin Ting, Tzu Yuan Fu, Rou Min Wang, Hong Jie Dai, Yung Chun Chang, Jitendra Jonnagaddala, Wen Lian Hsu

Graduate Institute of Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper's pregnancy stage. Another interesting application is to use the social media platforms to analyze users' health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.

Original language	English
Title of host publication	DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop
Publisher	Association for Computational Linguistics (ACL)
Pages	26-32
Number of pages	7
ISBN (Electronic)	9781948087070
Publication status	Published - 2017
Event	1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017 - Taipei, Taiwan Duration: Nov 27 2017 → …

Publication series

Name	DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop

Conference

Conference	1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017
Country/Territory	Taiwan
City	Taipei
Period	11/27/17 → …

ASJC Scopus subject areas

Software
Linguistics and Language
Language and Linguistics
Artificial Intelligence

Cite this

Huang, Y. J., Su, C. H., Chang, Y. C., Ting, T. H., Fu, T. Y., Wang, R. M., Dai, H. J., Chang, Y. C., Jonnagaddala, J., & Hsu, W. L. (2017). Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. In DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop (pp. 26-32). (DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop). Association for Computational Linguistics (ACL).

Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. / Huang, Yi Jie; Su, Chu Hsien; Chang, Yi Chun et al.
DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop. Association for Computational Linguistics (ACL), 2017. p. 26-32 (DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huang, YJ, Su, CH, Chang, YC, Ting, TH, Fu, TY, Wang, RM, Dai, HJ, Chang, YC, Jonnagaddala, J & Hsu, WL 2017, Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. in DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop. DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop, Association for Computational Linguistics (ACL), pp. 26-32, 1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017, Taipei, Taiwan, 11/27/17.

Huang YJ, Su CH, Chang YC, Ting TH, Fu TY, Wang RM et al. Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. In DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2017. p. 26-32. (DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop).

Huang, Yi Jie ; Su, Chu Hsien ; Chang, Yi Chun et al. / Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop. Association for Computational Linguistics (ACL), 2017. pp. 26-32 (DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop).

@inproceedings{b6034574ad284e81acd8fd7756767b9a,

title = "Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms",

abstract = "The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper's pregnancy stage. Another interesting application is to use the social media platforms to analyze users' health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.",

author = "Huang, {Yi Jie} and Su, {Chu Hsien} and Chang, {Yi Chun} and Ting, {Tseng Hsin} and Fu, {Tzu Yuan} and Wang, {Rou Min} and Dai, {Hong Jie} and Chang, {Yung Chun} and Jitendra Jonnagaddala and Hsu, {Wen Lian}",

note = "Funding Information: We are grateful for the constructive comments from three anonymous reviewers. This work was supported by grant MOST106-31-1E-4001-002 and MOST105-2221-E-001-008-MY3 from the Ministry of Science and Technology, Taiwan. Publisher Copyright: {\textcopyright} 2017 AFNLP; 1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017 ; Conference date: 27-11-2017",

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AU - Huang, Yi Jie

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AU - Ting, Tseng Hsin

AU - Fu, Tzu Yuan

AU - Wang, Rou Min

AU - Dai, Hong Jie

AU - Chang, Yung Chun

AU - Jonnagaddala, Jitendra

AU - Hsu, Wen Lian

N1 - Funding Information: We are grateful for the constructive comments from three anonymous reviewers. This work was supported by grant MOST106-31-1E-4001-002 and MOST105-2221-E-001-008-MY3 from the Ministry of Science and Technology, Taiwan. Publisher Copyright: © 2017 AFNLP

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AB - The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper's pregnancy stage. Another interesting application is to use the social media platforms to analyze users' health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.

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Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms

Abstract

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Conference

ASJC Scopus subject areas

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