The role of emotion in P2P microfinance funding: A sentiment analysis approach

Abstract Online peer-to-peer (P2P) lending platforms are gaining popularity for providing financing and loans to small and micro entrepreneurs, particularly in developing parts of the world. This study investigates how individual borrowers in the P2P platform can improve the chance of their loans being funded. Based on theories related to cognitive and affective aspects of information processing, a set of loan description features is identified and evaluated based on their influence on funding success. Sentiment analysis, a text mining technique, is used to analyze and extract emotions from an unstructured P2P loan data set. The results reveal valuable information in the P2P lending context such that, in the absence of market interest rates, borrowers can improve the chance of funding success by improving the textual quality of their loan descriptions in terms of readability and linguistic correctness. In addition, borrowers can make the loan descriptions more attractive to potential lenders by expressing certain emotions in the descriptions.

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