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How review sentiment influences ratings in Generative AI applications: Insights from VADER and LDA analysis

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  • Meng, Yuebin
  • Park, Sangchul
  • Um, Geumchul

Abstract

Generative AI applications have emerged as a crucial medium for users' daily intelligent interactions with user sentiment tendencies serving as a vital factor influencing product optimization and market competition. The purpose of this study was to explore the relationship between the title and review sentiment and rating and its boundary conditions. To this end, this study collected 100,010 user reviews from nine major generative AI applications in the U.S. App Store, using integrated VADER sentiment analysis scores with LDA topic weights. The results showed that the sentiment scores of reviews and titles have a significant positive effect on user ratings, respectively. Moreover, this study verified (1) the negative moderating effect of the review length and title length and that (2) the value-related topics enhance the review sentiment score, while technology-related and functionality-related topics weaken the review sentiment score. These findings provide empirical evidence for product managers and operations teams of generative AI platforms for optimizing their products and securing a competitive edge in the market.

Suggested Citation

  • Meng, Yuebin & Park, Sangchul & Um, Geumchul, 2026. "How review sentiment influences ratings in Generative AI applications: Insights from VADER and LDA analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:joreco:v:88:y:2026:i:c:s096969892500339x
    DOI: 10.1016/j.jretconser.2025.104560
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