Social Media Sentiment and Gender on Peer-To-Peer Lending Performance: Machine Learning Approaches
Journal Title: International Journal of Social Science And Human Research - Year 2024, Vol 7, Issue 09
Abstract
One innovation brought about by fintech is peer-to-peer lending. However, in recent years with rapid growth, unfortunately, there are still many negative sentiments about the peer-to-peer lending industry. Especially with the advancement of technology and the internet, all information about peer-to-peer lending is easily accessible through social media. The amount of negative information about peer-to-peer lending can influence opinions or sentiments towards the performance of peer-to-peer lending platforms and invite the public to comment on social media. Additionally, the gender factor of the information provider also affects the performance of the peer-to-peer market. Based on the explanation above, there has never been any previous research conducted in Indonesia, so this study will examine how the influence of social media information sentiment and the gender factor of the information provider affects peer-to-peer lending performance. The analysis method used is logistic regression, and the data used is obtained through machine learning using the Python programming language on Google Collab from social media opinions and the gender that provides opinions on social media. Meanwhile, the financial performance of peer-to-peer lending is taken from the annual performance report of the Financial Services Authority (OJK) by default. From the results of this study, it was found that the gender of the information provider affects the financial performance of peer-to-peer lending. However, simultaneously, social media sentiment and the gender of the information provider affect the financial performance of peer-to-peer lending.
Authors and Affiliations
Muhammad Farhansyah Mondari, Putri Fariska
Keywords
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- EP ID EP744948
- DOI 10.47191/ijsshr/v7-i09-30
- Views 53
- Downloads 0
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