Automatic Emoticons Insertion System based on Acoustic Information of User Voice: 3rd Report on Hyper-parameter tuning of SVM and Visual Representation

This study proposes an Automatic Emoticons Insertion System that enhances text communication by estimating user emotions from speech and automatically inserting corresponding emoticons. The system uses Support Vector Machine (SVM) algorithms to classify emotions based on acoustic features extracted through the openSMILE toolkit. By optimizing SVM hyperparameters via grid search, the model achieved a significant improvement in classification accuracy, with the best performance at C= 3.997 and γ = 0.03071. Additionally, we introduce a visualization technique based on Plutchik’s Wheel of Emotions to represent the emotional nuances conveyed by emoticons. This approach integrates emotion recognition into digital communication, improving emotional clarity and reducing misinterpretation.