From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis

TL;DR

This paper introduces a novel method using ChatGPT for text augmentation to enhance sentiment analysis, enabling smaller models to perform comparably or better than larger ones while reducing computational costs.

Contribution

It presents a new approach leveraging ChatGPT for synthetic data generation to improve sentiment analysis efficiency and effectiveness of smaller models.

Findings

Smaller models achieve performance comparable to larger models.

Synthetic data significantly boosts sentiment analysis accuracy.

Reduced computational cost and inference time.

Abstract

In the era of artificial intelligence, data is gold but costly to annotate. The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT for text augmentation in sentiment analysis. We leverage ChatGPT's generative capabilities to create synthetic training data that significantly improves the performance of smaller models, making them competitive with, or even outperforming, their larger counterparts. This innovation enables models to be both efficient and effective, thereby reducing computational cost, inference time, and memory usage without compromising on quality. Our work marks a key advancement in the cost-effective development and deployment of robust sentiment analysis models.