Abstract

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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalLanguage Resources and Evaluation
DOIs
StateAccepted/In press - May 18 2018

Fingerprint

dictionary
social media
market segmentation
systems analysis
resources
customer
Sentiment
Slang Words
Dictionary
Slang
economics

Keywords

  • Sentiment classification
  • Sentiment lexicon
  • Slang words
  • Social media

ASJC Scopus subject areas

  • Language and Linguistics
  • Education
  • Linguistics and Language
  • Library and Information Sciences

Cite this

SlangSD : building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification. / Wu, Liang; Morstatter, Fred; Liu, Huan.

In: Language Resources and Evaluation, 18.05.2018, p. 1-14.

Research output: Contribution to journalArticle

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abstract = "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.",
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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.

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