An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media

Neha Warikoo, Yung Chun Chang, Hong Jie Dai, Wen Lian Hsu

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

1 Citation (Scopus)

Abstract

Extensive use of social media for communication has made it a desired resource in human behavior intensive tasks like product popularity, public polls and more recently for public health surveillance tasks such as lifestyle associated diseases and mental health. In this paper, we exploited Twitter data for detecting pregnancy cases and used tweets about pregnancy to study trigger terms associated with maternal physical and mental health. Such systems can enable clinicians to offer a more comprehensive health care in real time. Using a Twitter-based corpus, we have developed an ensemble Long-short Term Memory (LSTM) – Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN) network representation model to learn legitimate pregnancy cases discussed online. These ensemble representations were learned by a SVM classifier, which can achieve F1-score of 95% in predicting pregnancy accounts discussed in tweets. We also further investigate the words most commonly associated with physical disease symptoms ‘Distress’ and negative emotions ‘Annoyed’ sentiment. Results from our sentiment analysis study are quite encouraging, identifying more accurate triggers for pregnancy sentiment classes.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 14th Asia Information Retrieval Societies Conference, AIRS 2018, Proceedings
EditorsLun-Wei Ku, Jui-Feng Yeh, Liang-Chih Yu, Yuen-Hsien Tseng, Zhi-Hong Chen, Tetsuya Sakai, Jing Jiang, Lung-Hao Lee, Dae Hoon Park
PublisherSpringer Verlag
Pages3-15
Number of pages13
ISBN (Print)9783030035198
DOIs
Publication statusPublished - Jan 1 2018
Event14th Asia Information Retrieval Societies conference, AIRS 2018 - Taipei, Taiwan
Duration: Nov 28 2018Nov 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11292 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Asia Information Retrieval Societies conference, AIRS 2018
CountryTaiwan
CityTaipei
Period11/28/1811/30/18

Keywords

  • Ensemble deep learning
  • Health surveillance
  • Pregnancy health stats
  • Sentiment analysis
  • Text mining of Twitter data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Warikoo, N., Chang, Y. C., Dai, H. J., & Hsu, W. L. (2018). An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media. In L-W. Ku, J-F. Yeh, L-C. Yu, Y-H. Tseng, Z-H. Chen, T. Sakai, J. Jiang, L-H. Lee, & D. H. Park (Eds.), Information Retrieval Technology - 14th Asia Information Retrieval Societies Conference, AIRS 2018, Proceedings (pp. 3-15). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11292 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-03520-4_1