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Lender Trust on the P2P Lending: Analysis Based on Sentiment Analysis of Comment Text

Author

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  • Beibei Niu

    (College of Economics and Management, China Agricultural University, Beijing 100083, China)

  • Jinzheng Ren

    (College of Economics and Management, China Agricultural University, Beijing 100083, China)

  • Ansa Zhao

    (Agricultural Development Bank of China, Beijing 100045, China)

  • Xiaotao Li

    (College of Economics and Management, China Agricultural University, Beijing 100083, China)

Abstract

Lender trust is important to ensure the sustainability of P2P lending. This paper uses web crawling to collect more than 240,000 unique pieces of comment text data. Based on the mapping relationship between emotion and trust, we use the lexicon-based method and deep learning to check the trust of a given lender in P2P lending. Further, we use the Latent Dirichlet Allocation (LDA) topic model to mine topics concerned with this research. The results show that lenders are positive about P2P lending, though this tendency fluctuates downward with time. The security, rate of return, and compliance of P2P lending are the issues of greatest concern to lenders. This study reveals the core subject areas that influence a lender’s emotions and trusts and provides a theoretical basis and empirical reference for relevant platforms to improve their operational level while enhancing competitiveness. This analytical approach offers insights for researchers to understand the hidden content behind the text data.

Suggested Citation

  • Beibei Niu & Jinzheng Ren & Ansa Zhao & Xiaotao Li, 2020. "Lender Trust on the P2P Lending: Analysis Based on Sentiment Analysis of Comment Text," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3293-:d:347072
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    References listed on IDEAS

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    Cited by:

    1. Wang, Chao & Zhang, Yue & Zhang, Weiguo & Gong, Xue, 2021. "Textual sentiment of comments and collapse of P2P platforms: Evidence from China's P2P market," Research in International Business and Finance, Elsevier, vol. 58(C).
    2. Honglin Xiong & Chongjun Fan & Hongmin Chen & Yun Yang & Collins Opoku ANTWI & Xiaomao Fan, 2022. "A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model," SAGE Open, , vol. 12(1), pages 21582440211, January.

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    Keywords

    P2P lending; public trust; sentiment analysis;
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