Citation

Tirdad K, Dela Cruz A, Sadeghian A, Cusimano M. Brain Inform. 2021; 8(1): e12.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1186/s40708-021-00134-4

PMID

unavailable

Abstract

Annually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substance and alcohol use, and violent behaviors. A significant body of research over the last decade has led to changes in policies and laws intended to reduce the incidence and burden of concussions. However, it is also clear that youth engaging in high-risk activities like sport often underreport concussion, while others may embellish reports for specific purposes. For such policies and laws to work, they must operate effectively within a facilitative social context so understanding the culture around concussion becomes essential to reducing concussion and its consequences. We present an automated deep neural network approach to analyze tweets with sport-related concussion context to identify the general public's sentiment towards concerns in sport-related concussion. A single-layer and multi-layer convolutional neural networks, Long Short-Term Memory (LSTM) networks, and Bidirectional LSTM were trained to classify the sentiments of the tweets. Afterwards, we train an ensemble model to aggregate the predictions of our networks to provide a final decision of the tweet's sentiment. The system achieves an evaluation F1 score of 62.71% based on Precision and Recall. The trained system is then used to analyze the tweets in the FIFA World Cup 2018 to measure audience reaction to events involving concussion. The neural network system provides an understanding of the culture around concussion through sentiment analysis.

Language: en

Keywords

Machine learning; Concussion; Traumatic brain injuries; Artificial intelligence; Sentiment analysis; Deep learning