Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Mengyang Gao, Jun Wang, Ou Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Purpose: Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China. Design/methodology/approach: After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis. Findings: The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies. Practical implications: The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types. Originality/value: This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

Original languageEnglish
Pages (from-to)1356-1384
Number of pages29
JournalIndustrial Management and Data Systems
Issue number4
Publication statusPublished - 12 Apr 2024


  • Commodity recommendation
  • Online review
  • Sentiment analysis
  • User-generated content


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