  Stance detection is the task of inferring viewpoint towards a given topic or
entity either being supportive or opposing. One may express a viewpoint towards
a topic by using positive or negative language. This paper examines how the
stance is being expressed in social media according to the sentiment polarity.
There has been a noticeable misconception of the similarity between the stance
and sentiment when it comes to viewpoint discovery, where negative sentiment is
assumed to mean against stance, and positive sentiment means in-favour stance.
To analyze the relation between stance and sentiment, we construct a new
dataset with four topics and examine how people express their viewpoint with
regards these topics. We validate our results by carrying a further analysis of
the popular stance benchmark SemEval stance dataset. Our analyses reveal that
sentiment and stance are not highly aligned, and hence the simple sentiment
polarity cannot be used solely to denote a stance toward a given topic.
