Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs

TL;DR

This paper presents a method using large language models like GPT and BERT to perform review sentiment classification without manual labeling, making machine learning accessible to small businesses and individuals.

Contribution

It introduces a novel approach that leverages LLMs to achieve high-accuracy review classification without the need for labeled data or extensive computational resources.

Findings

High classification accuracy achieved without manual labeling

Method accessible to users with limited technical expertise

Applicable across various datasets and review types

Abstract

With the internet's evolution, consumers increasingly rely on online reviews for service or product choices, necessitating that businesses analyze extensive customer feedback to enhance their offerings. While machine learning-based sentiment classification shows promise in this realm, its technical complexity often bars small businesses and individuals from leveraging such advancements, which may end up making the competitive gap between small and large businesses even bigger in terms of improving customer satisfaction. This paper introduces an approach that integrates large language models (LLMs), specifically Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT)-based models, making it accessible to a wider audience. Our experiments across various datasets confirm that our approach retains high classification accuracy without the need for manual labeling, expert knowledge in tuning and data annotation, or substantial computational power. By significantly lowering the barriers to applying sentiment classification techniques, our methodology enhances competitiveness and paves the way for making machine learning technology accessible to a broader audience.