To Be or Not to Be Demonstrative? Toward a Sentiment Analysis of the Platform Economy
Author
Abstract
This paper aims to explore how platform economy as a new business model diffuses in the modern social system. Methodologically, we use sentiment analysis of natural language processing to measure the changes in consumers’ sentiment to understand the dynamic process. We collect Foodpanda and Uber Eats’ online user reviews data from Google Play (35,909 in total) by web crawler, ranging from October 2016 to November 2019. Data are further divided into training set and testing set based on the sample size. Then, we compute monthly sentiment scores between -1 and 1. We found that users’ unfamiliarity became negative sentiment when Foodpanda and Uber Eats first entered Taiwan. Due to Foodpanda and Uber Eats’ increasingly improved service and active promotions, more users began to attend to the platform economy, leading to the increase of their sentiment score – Foodpanda in particular. As the platform market became mature, the increase in their scores slowed down. Furthermore, we develop a four-phase development model of platform economy and offer some practical implications.
