Document Type
Conference Proceeding
Publication Date
7-2017
Abstract
Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the temporal and spatial spread of Zika.
Repository Citation
Muppalla, R.,
Miller, M.,
Banerjee, T.,
& Romine, W. L.
(2017). Discovering Explanatory Models to Identify Relevant Tweets on Zika. .
https://corescholar.libraries.wright.edu/knoesis/1130
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
This paper was presented at the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017).