Method evidence record
Semi-supervised Sentiment Analysis
Semi-supervised sentiment analysis combines a small set of manually labeled text samples with a large pool of unlabeled text to train opinion classifiers. By propagating sentiment signals from labeled seeds to unlabeled data through self-training, label propagation, or consistency regularization, the approach achieves competitive accuracy without the cost of labeling large corpora.
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Semi-supervised Sentiment Analysis (Label Propagation and Self-Training for Opinion Mining)
Taxonomic method record · ml-model / deep-learning
- Zhu, X. (2005). Semi-Supervised Learning Literature Survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison. · URL
- Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. · DOI 10.1561/1500000011
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