Wang Dahai, Li Bing, Yan Xuebo
College of Management, Ocean University of China, Qingdao, China.
School of Software, Jiangxi Normal University, Nanchang, China.
Front Psychol. 2022 Feb 28;13:856409. doi: 10.3389/fpsyg.2022.856409. eCollection 2022.
Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition technology that integrates a number of emerging science and technology, and has good non-linear system fitting capabilities. However, using emotion recognition algorithm models to analyze and predict financial market and economic growth and development trends can yield more accurate prediction results. This article first gives a detailed introduction to the existing financial development and economic growth status and development trend forecasting problems, and then gives a brief overview of the concept of emotion recognition algorithms. Then, it describes the emotion recognition methods, including statistical emotion recognition methods, mixed emotion recognition methods, and emotion recognition methods based on knowledge technology, and conducts in-depth research on the three algorithm models of statistical emotion recognition methods, they are the support vector machine algorithm model, the artificial neural network algorithm model, and the long and short-term memory network algorithm model. Finally, these three algorithm models are applied to the financial market and economic growth and development trend prediction experiments. Experimental results show that the average absolute error of the three algorithms is below 25, which verifies that the emotion recognition algorithm has good operability and feasibility for the prediction of financial market and economic growth and development trends.
金融市场与经济增长及发展趋势可被视为一个极其复杂的系统，对这一复杂系统的深入研究与预测一直是经济学家及其他学者关注的焦点。情感识别算法是一种融合了多种新兴科技的模式识别技术，具有良好的非线性系统拟合能力。然而，运用情感识别算法模型来分析和预测金融市场与经济增长及发展趋势能够产生更为准确的预测结果。本文首先详细介绍了现有的金融发展与经济增长状况以及发展趋势预测问题，接着简要概述了情感识别算法的概念。然后，阐述了情感识别方法，包括统计情感识别方法、混合情感识别方法以及基于知识技术的情感识别方法，并对统计情感识别方法的三种算法模型进行了深入研究，它们分别是支持向量机算法模型、人工神经网络算法模型以及长短时记忆网络算法模型。最后，将这三种算法模型应用于金融市场与经济增长及发展趋势预测实验。实验结果表明，这三种算法的平均绝对误差均低于25，验证了情感识别算法对金融市场与经济增长及发展趋势预测具有良好的可操作性和可行性。
