Emotion Detection and Adjustment in Emails: A Solution for Teenagers in Mountainous Regions

Compared with teenagers in cities, unique groups such as teenagers living in mountainous areas have unique cultural experiences and language differences in using words to express emotions. This difference is reflected in aspects such as the use of local slang or dialects in emails. This article presents an improved BERT-based algorithmic model for detecting specific emotions in emails written by these teenagers and providing relevant feedback and emotional support. We classify adolescent emotions into: happiness, anger, surprise, sadness, fear and disgust. It was divided into training, validation and testing by analyzing the emails of 30 teenagers in the mountains. A combination of SVM, LSTM, and BERT algorithms was employed to enhance the emotion classification model's accuracy. The evaluation demonstrated that the BERT-based model notably outperformed others in both accuracy and F1-score.