@inproceedings{b479627b08dc48cabb1ed9e6eed51831,
title = "An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media",
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 {\textquoteleft}Distress{\textquoteright} and negative emotions {\textquoteleft}Annoyed{\textquoteright} sentiment. Results from our sentiment analysis study are quite encouraging, identifying more accurate triggers for pregnancy sentiment classes.",
keywords = "Ensemble deep learning, Health surveillance, Pregnancy health stats, Sentiment analysis, Text mining of Twitter data",
author = "Neha Warikoo and Chang, {Yung Chun} and Dai, {Hong Jie} and Hsu, {Wen Lian}",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 14th Asia Information Retrieval Societies conference, AIRS 2018 ; Conference date: 28-11-2018 Through 30-11-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-03520-4_1",
language = "English",
isbn = "9783030035198",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--15",
editor = "Lun-Wei Ku and Jui-Feng Yeh and Liang-Chih Yu and Yuen-Hsien Tseng and Zhi-Hong Chen and Tetsuya Sakai and Jing Jiang and Lung-Hao Lee and Park, {Dae Hoon}",
booktitle = "Information Retrieval Technology - 14th Asia Information Retrieval Societies Conference, AIRS 2018, Proceedings",
address = "Germany",
}