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Title
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Enhancing general sentiment lexicons for domain-specific use
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Author
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Abstract
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| Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity classification. SentProp is a framework for inducing domain-specific po- larities from word embeddings. We elaborate on SentProp by evaluating its use for enhancing DuOMan, a general-purpose lexicon, for use in the political domain. By adding only top senti- ment bearing words from the vocabulary and applying small polarity shifts in the general-purpose lexicon, we increase accuracy in an in-domain classification task. The enhanced lexicon performs worse than the original lexicon in an out-domain task, showing that the words we added and the polarity shifts we applied are domain-specific and do not translate well to an out-domain setting. |
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Language
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English
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Source (book)
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Proceedings of the 27th International Conference on Computational Linguistics, August, 2018, Sante Fe, New Mexico, USA
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Publication
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Association for Computational Linguistics
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2018
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ISBN
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978-1-948087-55-1
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Volume/pages
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p. 1056-1064
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Article Reference
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C18-1090
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Medium
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E-only publicatie
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Full text (open access)
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