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

  • Neha Warikoo
  • Yung-Chun ChangEmail author
  • Hong-Jie Dai
  • Wen-Lian Hsu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11292)

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.

Keywords

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

Notes

Acknowledgments

We are grateful to the anonymous reviewers for their insightful comments. This research was supported by the Ministry of Science and Technology of Taiwan under grant number MOST 106-2218-E-038-004-MY2, MOST 107-2634-F-001-005, and MOST 107-2319-B-400-001.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Neha Warikoo
    • 1
    • 2
    • 3
  • Yung-Chun Chang
    • 4
    Email author
  • Hong-Jie Dai
    • 5
  • Wen-Lian Hsu
    • 3
  1. 1.Institute of Biomedical Informatics, National Yang-Ming UniversityTaipeiTaiwan
  2. 2.Bioinformatics Program, International Graduate Program Taiwan, Institute of Information Science, Academia SinicaTaipeiTaiwan
  3. 3.Institute of Information Science, Academia SinicaTaipeiTaiwan
  4. 4.Graduate Institute of Data Science, Taipei Medical UniversityTaipeiTaiwan
  5. 5.Department of Computer Science and Information EngineeringNational Taitung UniversityTaitungTaiwan

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