Exploring large language models for the generation of synthetic training samples for aspect-based sentiment analysis in low resource settings

URN to cite this document:: urn:nbn:de:bvb:355-epub-594331
DOI to cite this document:: 10.5283/epub.59433

Hellwig, Nils Constantin

; Fehle, Jakob

; Wolff, Christian

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 28 Oct 2024 12:45

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Abstract

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in sentiment analysis, aiming to identify sentiment expressed towards specific aspects of an entity. This paper explores the use of Large Language Models (LLMs), specifically GPT-3.5-turbo and Llama-3-70B, for generating annotated data in Aspect-Based Sentiment Analysis (ABSA), aiming to address the scarcity of labelled datasets in the ...