@article{2114,
  author = {Hsuanwei Michelle Chen, Patricia C. Franks, Lois Evans},
  title = {Exploring Government Uses of Social Media through Twitter Sentiment Analysis},
  journal = {Journal of Digital Information Management},
  year = {2016},
  volume = {14},
  number = {5},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v14i5/jdimv14i5_2.pdf},
  abstract = {As social media becomes an important
platform for organizations to use to interact with users,
the ability to understand user opinions in social media
communications has gained increased attention. One of
the most popular approaches for exploring user opinions
is sentiment analysis, which employs natural language
processing, statistics, or machine learning to extract the
sentiment of a text unit in terms of positive or negative
attitudes. However, the effectiveness, interpretation, and
accuracy of sentiment analysis rely heavily on the context
in which it is conducted. In this paper, we investigate three
sentiment analysis techniques for Twitter use by
governments with their citizens, including a lexicon-based
approach, a machine learning-based approach, and a
hybrid approach. Our results reveal that, while each
technique is developed based upon different rationales,
the results are statistically robust and comparable. The
study provides new insights into sentiment analysis in
the context of government uses of social med},
}