Slangsd building expanding and using a sentiment dictionary of slang words for short text sentiment classification

Sentiment information about social media posts is increasingly considered 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 a challenging task for practitioners. Though some slang words are available in existing sentiment lexicons, with new slang being generated with emerging memes, a dedicated lexicon will be useful for researchers and practitioners. To this end, we propose to build a slang sentiment dictionary to aid sentiment analysis. It is laborious and time-consuming to collect a comprehensive list of slang words and label the sentiment polarity. We present an approach to leverage web resources to construct a Slang Sentiment Dictionary (SlangSD) that is easy to expand. 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 a sentiment analysis system.