The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Semi-supervised polarity lexicon induction
Open accessAbstract
We present an extensive study on the problem of detecting polarity of words. We consider the polarity of a word to be either positive or negative. For example, words such as good, beautiful, and wonderful are considered as positive words; whereas words such as bad, ugly, and sad are considered negative words. We treat polarity detection as a semi-supervised label propagation problem in a graph. In the graph, each node represents a word whose polarity is to be determined. Each weighted edge encodes a relation that exists between two words. Each node (word) can have two labels: positive or negative. We study this framework in two different resource availability scenarios using WordNet and OpenOffice thesaurus when WordNet is not available. We report our results on three different languages: English, French, and Hindi. Our results indicate that label propagation improves significantly over the baseline and other semi-supervised learning methods like Mincuts and Randomized Mincuts for this task.
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Pang, B., & Lee, L. (2004) · Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04 · doi:10.3115/1218955.1218990 · Cited by 3,301
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Learning from labeled and unlabeled data with label propagation
Zhu, X., & Ghahramani, Z. (2002) · Cited by 1,534
Measuring praise and criticism: Inference of semantic orientation from association
Turney, P. D., & Littman, M. L. (2003) · ACM Transactions on Information Systems · doi:10.1145/944012.944013 · Cited by 1,502
Predicting the semantic orientation of adjectives
Hatzivassiloglou, V., & McKeown, K. R. (1997) · Proceedings of the 35th annual meeting on Association for Computational Linguistics - · doi:10.3115/976909.979640 · Cited by 1,439
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Learning from Labeled and Unlabeled Data using Graph Mincuts
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WordNet: similarity - measuring the relatedness of concepts
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Cited by
Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
Attention-based LSTM for Aspect-level Sentiment Classification
Wang, Y., Huang, M., zhu, X., & Zhao, L. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1058 · Cited by 2,450
Using Opinion Mining Techniques in Tourism
Bucur, C. (2015) · Procedia Economics and Finance · doi:10.1016/s2212-5671(15)00471-2 · Cited by 84
Metadata
- Authors
- Rao, D., & Ravichandran, D.
- Venue
- Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09
- Published
- 2009
- Pages
- 675-682
- OpenAlex
- W2089173648
- Cited by
- 334
- References
- 21
- FWCI
- 11.52
- Type
- conference paper
- Language
- en
- Open access
- gold
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