1 research outputs found
Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses
Systems that exploit publicly available user generated content such as
Twitter messages have been successful in tracking seasonal influenza. We
developed a novel filtering method for Influenza-Like-Illnesses (ILI)-related
messages using 587 million messages from Twitter micro-blogs. We first filtered
messages based on syndrome keywords from the BioCaster Ontology, an extant
knowledge model of laymen's terms. We then filtered the messages according to
semantic features such as negation, hashtags, emoticons, humor and geography.
The data covered 36 weeks for the US 2009 influenza season from 30th August
2009 to 8th May 2010. Results showed that our system achieved the highest
Pearson correlation coefficient of 98.46% (p-value<2.2e-16), an improvement of
3.98% over the previous state-of-the-art method. The results indicate that
simple NLP-based enhancements to existing approaches to mine Twitter data can
increase the value of this inexpensive resource.Comment: 10 pages, 5 figures, IEEE HISB 2012 conference, Sept 27-28, 2012, La
Jolla, California, U