The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
Open accessAbstract
A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words. We show that our approach achieves state-of-the-art performance on inducing sentiment lexicons from domain-specific corpora and that our purely corpus-based approach outperforms methods that rely on hand-curated resources (e.g., WordNet). Using our framework, we induce and release historical sentiment lexicons for 150 years of English and community-specific sentiment lexicons for 250 online communities from the social media forum Reddit. The historical lexicons we induce show that more than 5% of sentiment-bearing (non-neutral) English words completely switched polarity during the last 150 years, and the community-specific lexicons highlight how sentiment varies drastically between different communities.
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Cites
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Distributed Representations of Words and Phrases and their Compositionality
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Mining and summarizing customer reviews
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When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks
LOUGHRAN, T., & MCDONALD, B. (2011) · The Journal of Finance · doi:10.1111/j.1540-6261.2010.01625.x · Cited by 6,015
Learning with Local and Global Consistency
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2003) · MPG.PuRe (Max Planck Society) · Cited by 3,864
Semi-supervised learning using Gaussian fields and harmonic functions
Zhu, X., Zoubin Ghahramani, A., & Lafferty, J. (2003) · Cited by 3,509
Lexicon-based methods for sentiment analysis
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Natural language based financial forecasting: a survey
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.98 · Cited by 306
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., & Jurafsky, D. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1304 · Cited by 164
“Since word-affect associations are highly domain dependent, we tailored an existing affect lexicon, the NRC Emotion Lexicon (Mohammad and Turney, 2013), to our domain via label propagation (Hamilton et al., 2016).”
MethodSelf-citationDe Silva, D., Ranasinghe, W., Bandaragoda, T., Adikari, A., Mills, N., Iddamalgoda, L., Alahakoon, D., Lawrentschuk, N., Persad, R., Osipov, E., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0205855 · Cited by 86
Gallagher, R. J., Frank, M. R., Mitchell, L., Schwartz, A. J., Reagan, A. J., Danforth, C. M., & Dodds, P. S. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00260-3 · Cited by 70
“Further, the relative weights of those words may be highly context dependent [3, 26]—regional dialects [32, 33], variations in slang [34], and other domain-specific usage [26, 35, 36] all affect how appropriate it is to compare across weighted averages.”
BackgroundMethodInfluential
Metadata
- Authors
- Hamilton, W. L., Clark, K., Leskovec, J., & Jurafsky, D.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Volume
- 2016
- Pages
- 595-605
- OpenAlex
- W2964325543
- Cited by
- 355
- References
- 56
- FWCI
- 21.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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