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

2024/10/17 by Nils Constantin Hellwig, Jakob Fehle, Christian Wolff · 1 citation
Computer Science · #Advanced Text Analysis Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling

paper · doi:10.1016/j.eswa.2024.125514

openalex publication_date 2024/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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 field. Two low-resource scenarios are considered, with 25 and 500 manually annotated examples available. In the 25-example scenario, adding synthetic examples generated through few-shot prompting resulted in F1 scores of 81.33 for Aspect Category Detection (ACD) and 71.71 for Aspect Category Sentiment Analysis (ACSA). For the 500-example scenario, synthetic data augmentation showed a notable gain only for the ACSA task, raising the F1 score from 84.54 to 86.70. • LLM-generated examples enhance performance in Aspect Category Detection (ACD). • Synthetic examples lead to an F1 score of 81.33 on the ACD task. • Llama-3-70B generated more linguistically diverse data than GPT-3.

Citations

• Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
• GPT-3: Its Nature, Scope, Limits, and Consequences
• Survey of Aspect-based Sentiment Analysis Datasets
• Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
• LLaMA: Open and Efficient Foundation Language Models
• The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks

Cited by

• Large Language Models for Subjective Language Understanding: A Survey

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• Survey of Aspect-based Sentiment Analysis Datasets
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