Emotion Classification Method of Financial News Based on Artificial Intelligence

Abstract

With the continuous development of economy, the economic development model is constantly changing. Especially since China’s entry into WTO, the scale of economic development has reached a new height. The continuous development of economy makes the financial news module evolve towards specialization. However, with the emergence of Internet of Things technology, a large number of data appear in the network, which brings some difficulties to the classification and analysis of data economy. Emotion classification refers to the complexity and diversity of people’s emotions. It can be classified from different observation angles. Because the core content of emotion is value, human emotion should be classified mainly according to the different characteristics of the movement and change of value relationship it reflects. This paper is aimed at studying the emotional classification method of financial news based on artificial intelligence and expecting to use artificial intelligence technology and classification method to classify financial news. It allows more people to know the implied information of financial information and promotes economic development. Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. This paper mainly summarizes the topic selection characteristics and subdivision topic selection characteristics of financial data news through quantitative and qualitative methods and explores the classification of financial news. In this paper, a simplified classification algorithm based on convolution function is proposed for the classification of traditional financial news networks. The experimental results show that the classification accuracy of artificial intelligence method is improved by 4% compared with the traditional emotion classification method, and the classification accuracy of positive emotion is lower than that of negative emotion by 2%.