Using natural language processing to extract health-related causality from Twitter messages
This work addresses the challenge of understanding health conditions from social media data for public health researchers, but it is incremental as it applies existing NLP patterns to a new domain.
The paper tackled the problem of extracting health-related causal relations from Twitter messages using NLP, achieving an average precision between 74.59% and 92.27% on topics like stress, insomnia, and headache.
Twitter messages (tweets) contain various types of information, which include health-related information. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily life. In this work, we evaluated an approach to extracting causal relations from tweets using natural language processing (NLP) techniques. We focused on three health-related topics: stress", "insomnia", and "headache". We proposed a set of lexico-syntactic patterns based on dependency parser outputs to extract causal information. A large dataset consisting of 24 million tweets were used. The results show that our approach achieved an average precision between 74.59% and 92.27%. Analysis of extracted relations revealed interesting findings about health-related in Twitter.