SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
This addresses the difficulty of measuring sentiment in short, informal social media text for applications like customer segmentation and market understanding, though it is incremental as it builds on existing sentiment analysis methods.
The authors tackled the problem of sentiment analysis in social media by building the first sentiment dictionary of slang words, called SlangSD, which is publicly available and shown to improve sentiment classification.
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.