@inproceedings{huang-etal-2017-incorporating,
title = "Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms",
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
Jonnagaddala, Jitendra and
Hsu, Wen-Lian",
editor = "Jonnagaddala, Jitendra and
Dai, Hong-Jie and
Chang, Yung-Chun",
booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5804/",
pages = "26--32",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
%A Huang, Yi-Jie
%A Su, Chu Hsien
%A Chang, Yi-Chun
%A Ting, Tseng-Hsin
%A Fu, Tzu-Yuan
%A Wang, Rou-Min
%A Dai, Hong-Jie
%A Chang, Yung-Chun
%A Jonnagaddala, Jitendra
%A Hsu, Wen-Lian
%Y Jonnagaddala, Jitendra
%Y Dai, Hong-Jie
%Y Chang, Yung-Chun
%S Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
%D 2017
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F huang-etal-2017-incorporating
%X 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.
%U https://aclanthology.org/W17-5804/
%P 26-32
Markdown (Informal)
[Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms](https://aclanthology.org/W17-5804/) (Huang et al., 2017)
ACL
- 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, and Wen-Lian Hsu. 2017. Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. In Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017), pages 26–32, Taipei, Taiwan. Association for Computational Linguistics.