  Sentiment in social media is increasingly considered as an important resource
for customer segmentation, market understanding, and tackling other
socio-economic issues. However, sentiment in social media is difficult to
measure since user-generated content is usually short and informal. Although
many traditional sentiment analysis methods have been proposed, identifying
slang sentiment words remains untackled. One of the reasons is that slang
sentiment words are not available in existing dictionaries or sentiment
lexicons. To this end, we propose to build the first sentiment dictionary of
slang words to aid sentiment analysis of social media content. It is laborious
and time-consuming to collect and label the sentiment polarity of a
comprehensive list of slang words. We present an approach to leverage web
resources to construct an extensive Slang Sentiment word Dictionary (SlangSD)
that is easy to maintain and extend. SlangSD is publicly available for research
purposes. We empirically show the advantages of using SlangSD, the newly-built
slang sentiment word dictionary for sentiment classification, and provide
examples demonstrating its ease of use with an existing sentiment system.
