Crafting clarity: Leveraging large language models to decode consumer reviews

S.V. Praveen, Pranshav Gajjar, Rajeev Kumar Ray and Ashutosh Dutt

Journal of Retailing and Consumer Services, 2024, vol. 81, issue C

Abstract: Large Language Models (LLMs) have emerged as powerful tools for understanding consumer perceptions and extracting insights from unstructured textual data. This study investigates the effectiveness of LLMs in comprehending consumer opinions, particularly in service industries. We fine-tuned four prominent LLMs—Falcon-7B, MPT-7B, GPT-2, and BERT—using 1,031,478 consumer reviews and assessed their ability to identify topics, emotions, and sentiments. Our results indicate that Falcon-7B excels in accuracy and reliability for complex tasks. This study is the first, to our knowledge, to fine-tune Large Language Models (LLMs) specifically for consumer data, showcasing the efficacy of attention mechanisms in extracting valuable insights. Our findings offer strategic decision-making insights for the service industry and underscore the transformative potential of LLMs in business intelligence from consumer feedback.

Keywords: Natural language processing; Large language models; Fine-tuning; Transfer learning; Deep learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924002716

DOI: 10.1016/j.jretconser.2024.103975

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