The Impact of News on Public-Private Partnership Stock Price in China via Text Mining Method

Abstract

In data analytics, the application of text analysis is always challenging, in particular, when performing the text mining of Chinese characters. This study aims to use the micro-blog data created by the users to conduct text mining and analysis of the impact of stock market performances in China. Based upon Li's instance labeling method, this chapter examines the correlation between social media information and a public-private partnership (PPP)-related company stock prices. The authors crawled the data from EastMoney platform via a web crawler and obtained a total of 79,874 language data from 10 January 2017 to 28 November 2019. The total material data obtained is 79,616, which the authors use for specific training in the financial corpus. The findings of this chapter indicate that the investor investment sentiment has a certain impact on the stock price movement of selected stocks in the PPP sector.