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This approach is known as few-shot prompting, where you include a few labeled examples to guide the model on how to classify sentiment.
Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified: This approach uses few-shot learning, where you give the model clear examples of text passages labeled with sentiment (positive or negative). By providing these examples in the prompt, the model is better able to understand the task and generalize it to classify the new text passage correctly. This is a common and effective strategy for tasks like sentiment analysis.