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

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.

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