The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Sarcasm as Contrast between a Positive Sentiment and Negative Situation
Open accessAbstract
A common form of sarcasm on Twitter consists of a positive sentiment contrasted with a negative situation.For example, many sarcastic tweets include a positive sentiment, such as "love" or "enjoy", followed by an expression that describes an undesirable activity or state (e.g., "taking exams" or "being ignored").We have developed a sarcasm recognizer to identify this type of sarcasm in tweets.We present a novel bootstrapping algorithm that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets.We show that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05 · doi:10.3115/1220575.1220619 · Cited by 3,345
Opinion observer: analyzing and comparing opinions on the Web
Liu, B., Hu, M., & Cheng, J. (2005) · Proceedings of the 14th international conference on World Wide Web - WWW '05 · doi:10.1145/1060745.1060797 · Cited by 1,597
A new ANEW: Evaluation of a word list for sentiment analysis in microblogs
Nielsen, F. Å. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.2903 · Cited by 712
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
Owoputi, O., O'Connor, B., Dyer, C., Gimpel, K., Schneider, N., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473408 · Cited by 662
Identifying Sarcasm in Twitter: A Closer Look
González‐Ibáñez, R., Muresan, S., & Wacholder, N. (2011) · Cited by 627
OpinionFinder: a system for subjectivity analysis
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2005) · Proceedings of HLT/EMNLP on Interactive Demonstrations - · doi:10.3115/1225733.1225751 · Cited by 469
Self-citationSemi-supervised recognition of sarcastic sentences in Twitter and Amazon
Davidov, D., Tsur, O., & Rappoport, A. (2010) · Cited by 424
Good Friends, Bad News - Affect and Virality in Twitter
Hansen, L. K., Arvidsson, A., Nielsen, F. A., Colleoni, E., & Etter, M. (2011) · Communications in Computer and Information Science · doi:10.1007/978-3-642-22309-9_5 · Cited by 361
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Cited by
Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854
“The latter includes annotations for sentiment (Liu, 2012) and for figurative language or sarcasm (e.g., Riloff et al., 2013; Ptáˇcek et al., 2014).”
BackgroundA survey on sentiment analysis challenges
Hussein, D. M. E.-D. M. (2018) · Journal of King Saud University - Engineering Sciences · doi:10.1016/j.jksues.2016.04.002 · Cited by 659
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Poria, S., Cambria, E., Hazarika, D., & Vij, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.08815 · Cited by 292
Metadata
- Authors
- Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R.
- Venue
- Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
- Published
- 2013
- Pages
- 704-714
- OpenAlex
- W2250710744
- Cited by
- 587
- References
- 21
- FWCI
- 22.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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