COMPARISON OF ZERO AND FEW-SHOT LEARNING APPROACH USING THE LLMS FOR SENTIMENT ANALYSIS IN SERBIAN LITERATURE

Goal: Assess if newer versions of LLMs offer more consistent, efficient, and potentially less biased sentiment annotation.Comparison: With previous research on srpELTeC Sentiment Analysis employing Mistral 7B model.Impact: Advance sentiment data processing and interpretation across various applications.Significance for Low-Resource Languages: Highlight challenges and benefits for languages like Serbian, where annotated corpora are scarce, limiting traditional sentiment analysis models.