Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis
Computation and Language
2015-09-28 v1
Abstract
State of the art benchmarks for Twitter Sentiment Analysis do not consider the fact that for more than half of the tweets from the public stream a distinct sentiment cannot be chosen. This paper provides a new perspective on Twitter Sentiment Analysis by highlighting the necessity of explicitly incorporating uncertainty. Moreover, a dataset of high quality to evaluate solutions for this new problem is introduced and made publicly available.
Keywords
Cite
@article{arxiv.1509.07612,
title = {Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis},
author = {Nils Haldenwang and Oliver Vornberger},
journal= {arXiv preprint arXiv:1509.07612},
year = {2015}
}
Comments
3 pages, 1 figure, accepted at GSCL '15