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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 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 languageEnglish
Title of host publicationDDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages26-32
Number of pages7
ISBN (Electronic)9781948087070
Publication statusPublished - 2017
Event1st 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

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

Conference

Conference1st 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/TerritoryTaiwan
CityTaipei
Period11/27/17 → …

ASJC Scopus subject areas

  • Software
  • Linguistics and Language
  • Language and Linguistics
  • Artificial Intelligence

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