Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend

Area of Science:

• Economics
• Computer Science
• Data Science

Background:

• Financial markets and economic growth present complex systems challenging traditional forecasting methods.
• Emotion recognition algorithms offer advanced non-linear system fitting capabilities.

Purpose of the Study:

• To explore the application of emotion recognition algorithms in predicting financial market and economic growth trends.
• To evaluate the effectiveness of specific statistical emotion recognition models for economic forecasting.

Main Methods:

• Overview of emotion recognition concepts and methods (statistical, mixed, knowledge-based).
• In-depth research on Support Vector Machine (SVM), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithm models.
• Application of SVM, ANN, and LSTM models to financial market and economic trend prediction experiments.

Main Results:

• Experimental application of SVM, ANN, and LSTM models to financial and economic trend prediction.
• Achieved an average absolute error below 25 for all three tested algorithms.
• Demonstrated the operability and feasibility of emotion recognition algorithms in this domain.

Conclusions:

• Emotion recognition algorithms, particularly SVM, ANN, and LSTM, are effective tools for predicting financial market and economic growth trends.
• The study validates the practical applicability and accuracy of these advanced computational methods in economic forecasting.