Emotion Recognition Algorithm Based on Gaussian Mixture Model

This emotion recognition algorithm utilizes Gaussian Mixture Models (GMM) and is implemented using MATLAB programming language. The primary objective of this algorithm is to analyze and process textual data to identify emotional tendencies within the text. Through classification and labeling of textual emotions, we can better understand and analyze emotional information embedded in text data. MATLAB serves as a powerful computational tool that enables efficient implementation of this algorithm and facilitates emotion recognition and analysis on large-scale text datasets. The implementation involves several key steps: feature extraction from text data using appropriate linguistic features, GMM parameter estimation through Expectation-Maximization (EM) algorithm, and emotion classification based on maximum likelihood estimation. The MATLAB implementation typically utilizes functions from Statistics and Machine Learning Toolbox, including gmdistribution for GMM modeling and fitgmdist for parameter fitting. The algorithm handles high-dimensional feature spaces effectively and provides probabilistic emotion classification outcomes, making it particularly suitable for handling ambiguous emotional expressions in text. Key implementation components include: preprocessing text data into numerical feature vectors, training multiple GMM components corresponding to different emotion categories, and developing classification logic that compares new text samples against trained models using likelihood calculations. The MATLAB code structure typically involves separate modules for data preprocessing, model training, and emotion prediction, ensuring modular and maintainable implementation.