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Related Questions
- What are the limitations of human annotators in accurately labeling sentiment in text data with sarcasm or idioms?
- How do cultural differences impact human annotators' perspectives on sentiment analysis, particularly in texts from diverse linguistic backgrounds?
- What are common biases that human annotators may introduce during sentiment analysis, and how can they be mitigated?
- What are some strategies for improving human annotators' consistency when labeling sentiment in text data?
- Can human annotators accurately capture nuanced sentiment in text data that contains figurative language or ambiguities?
- How does the context of the text affect human annotators' sentiment analysis, and what techniques can be used to account for this?
- What are some common pitfalls or pitfalls that human annotators may fall into during sentiment analysis, such as confirmation bias or anchoring bias?
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