@article {779,
	title = {Challenges of Sentiment Analysis for Dynamic Events},
	year = {2017},
	abstract = {<p><span style="font-family: sans-serif; font-size: 15px;">Efforts to assess people{\textquoteright}s sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people{\textquoteright}s attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis{\textquoteright}s Twitris system.</span></p>
},
	author = {Monireh Ebrahimi and Amir Hossein Yazdavar and Amit Sheth}
}
