  Sentiment lexicons are instrumental for sentiment analysis. One can use a set
of sentiment words provided in a sentiment lexicon and a lexicon-based
classifier to perform sentiment classification. One major issue with this
approach is that many sentiment words are domain dependent. That is, they may
be positive in some domains but negative in some others. We refer to this
problem as domain polarity-changes of words. Detecting such words and
correcting their sentiment for an application domain is very important. In this
paper, we propose a graph-based technique to tackle this problem. Experimental
results show its effectiveness on multiple real-world datasets.
