TY - JOUR
T1 - SlangSD
T2 - building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification
AU - Wu, Liang
AU - Morstatter, Fred
AU - Liu, Huan
N1 - Funding Information:
We would like to thank DMML lab members for their feedback and help in this work. The work is funded, in part, by ONR N00014-16-1-2257 and the Department of Defense under the MINERVA initiative through the ONR N000141310835.
Publisher Copyright:
© 2018, Springer Science+Business Media B.V., part of Springer Nature.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - Sentiment classification
KW - Sentiment lexicon
KW - Slang words
KW - Social media
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U2 - 10.1007/s10579-018-9416-0
DO - 10.1007/s10579-018-9416-0
M3 - Article
AN - SCOPUS:85047121277
SN - 1574-020X
VL - 52
SP - 839
EP - 852
JO - Language Resources and Evaluation
JF - Language Resources and Evaluation
IS - 3
ER -