Application of Informetrics on Financial Network Text Mining Based on Affective Computing. Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Huang, Anzhong; Zhang, Yuling; Peng, Jianping; Chen, Hong
• Is Part Of: Information processing & management. Volume 59:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: informetrics -- SparkR -- Distributed computing -- affective computing; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102822
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: The research of the paper is an expansion of the application field of the informetrics on network text mining. This paper constructs a SparkR platform. This paper tries to explore the application of affective computing of the online financial text data in predicting the future movement of exchange rate. Abstract: With the rapid development of internet, text data is becoming richer, but most part of them is unstructured. So compared to statistics data, the text data is more difficult to be utilized. How to apply the informetrics on financial network text mining is a supplement to the traditional research methods of finance. The paper tries to forecast exchange rate volatility through informetrics on financial network text mining by means of affective computing. We find that if the amount of informetrics on network is used during predicting, only the peak and valley values of its volatility and are synchronous with the volatility of exchange rate. While the volatility of emotional intensity of words of informetrics on network in text data can accurately predict not only the drastic volatility of exchange rate, but also the moderate volatility.