Peer-to-peer Online Lending Sentiment Analysis

Abstract

Peer-to-Peer Lending online is one of the fintech applications, it has grown in popularity in recent years, attracting many people due to its ease of use, friendliness, and lack of regulation compared to banks. Because of these reasons, it attracts many researchers examined it from various perspectives. This paper focuses on sentiment analysis, world cloud to see the most frequent words used and topic modelling to analyze people's opinions about lending online. As we all know, the internet has become our everyday tool, and many people can use it to provide recommendations and warnings about anything. Using RapidMiner to extract data from Twitter each keyword can extract to 500 raws, use the same tool to do the preprocessing and text preparation and then apply sentiment analysis and topic modelling to the extracted data, the results show that people have positive opinions of the majority of peer-to-peer lending online platforms.