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Related Questions
- What are the key differences between active learning and passive learning in the context of data annotation for sentiment analysis?
- How can active learning be used to identify the most informative samples for human annotators to label, reducing the need for large-scale labeling efforts?
- What are some strategies for selecting the right instance selection methods for active learning in sentiment analysis, and how do they impact the overall performance of the model?
- Can active learning be used to adapt to changing user preferences and sentiment patterns in conversational AI, and if so, how?
- What are the potential challenges in implementing active learning for data annotation in sentiment analysis, and how can they be mitigated?
- How can active learning be used to improve the transferability of sentiment analysis models to new domains or tasks, such as text classification or topic modeling?
- What are the trade-offs between active learning and traditional supervised learning in terms of labeling effort, model performance, and computational resources?
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