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Crafting clarity: Leveraging large language models to decode consumer reviews

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

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.

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  • Praveen, S.V. & Gajjar, Pranshav & Ray, Rajeev Kumar & Dutt, Ashutosh, 2024. "Crafting clarity: Leveraging large language models to decode consumer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924002716
    DOI: 10.1016/j.jretconser.2024.103975
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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Margaret Roberts & Brandon Stewart & Tingley, Dustin & Edoardo Airoldi, 2013. "The structural topic model and applied social science," Working Paper 132666, Harvard University OpenScholar.
    3. Bharati Sanjay Ainapure & Reshma Nitin Pise & Prathiba Reddy & Bhargav Appasani & Avireni Srinivasulu & Mohammad S. Khan & Nicu Bizon, 2023. "Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    4. Wu, Jia-Jhou & Chang, Sue-Ting, 2020. "Exploring customer sentiment regarding online retail services: A topic-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    5. Kumar, Avinash & Chakraborty, Shibashish & Bala, Pradip Kumar, 2023. "Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    6. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    7. Sánchez-Franco, Manuel J. & Arenas-Márquez, Francisco J. & Alonso-Dos-Santos, Manuel, 2021. "Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    8. Yue Kang & Zhao Cai & Chee-Wee Tan & Qian Huang & Hefu Liu, 2020. "Natural language processing (NLP) in management research: A literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 139-172, April.
    9. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    10. Basu, Bibaswan & Sebastian, M.P. & Kar, Arpan Kumar, 2024. "What affects the promoting intention of mobile banking services? Insights from mining consumer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    11. Luong, Van Ha & Tarquini, Annalisa & Anadol, Yaprak & Klaus, Phil & Manthiou, Aikaterini, 2024. "Is digital fashion the future of the metaverse? Insights from YouTube comments," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    12. Sparks, Beverley A. & Browning, Victoria, 2011. "The impact of online reviews on hotel booking intentions and perception of trust," Tourism Management, Elsevier, vol. 32(6), pages 1310-1323.
    13. Kim, Da Yeon & Kim, Sang Yong, 2023. "Investigating the effect of customer-generated content on performance in online platform-based experience goods market," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    14. Stanca, Liana & Dabija, Dan-Cristian & Câmpian, Veronica, 2023. "Qualitative analysis of customer behavior in the retail industry during the COVID-19 pandemic: A word-cloud and sentiment analysis approach," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
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    1. Mustak, Mekhail & Hallikainen, Heli & Laukkanen, Tommi & Plé, Loïc & Hollebeek, Linda D. & Aleem, Majid, 2024. "Using machine learning to develop customer insights from user-generated content," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    2. Michal Maj & Damian Pliszczuk & Patryk Marek & Weronika Wilczewska & Bartosz Przysucha & Tomasz Rymarczyk, 2024. "Optimizing Customer Support Using Text2SQL to Query Natural Language Databases," European Research Studies Journal, European Research Studies Journal, vol. 0(Special B), pages 426-438.

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