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Related Questions
- What are some strategies for active learning to reduce the amount of annotated data required for sentiment analysis?
- How can transfer learning be applied to leverage pre-trained models and reduce annotation costs?
- What are some techniques for weak supervision, such as using noisy labels or self-training, to reduce annotation costs?
- Can multi-task learning be used to improve sentiment analysis performance while reducing annotation costs?
- What are some methods for semi-supervised learning, such as using unlabeled data or generating synthetic data, to reduce annotation costs?
- How can ensemble methods, such as stacking or bagging, be used to improve sentiment analysis performance while reducing annotation costs?
- What are some techniques for reducing annotation costs by using smaller datasets or subsets of the data, such as using a stratified sampling approach?
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