Stress Expression Identification Model for Emotion-Driven Association Calculation over Social Network Blogs

Emotion and stress expression are deeply intertwined. Analyzing blog data from social network platforms reveals that specific emotions often appear simultaneously with expressions indicating stress, and emotional expressions in different stress scenarios also display unique patterns. This paper introduces the Stress Expression Identification (SEI) model based on Emotion Sequence Pattern Analysis (ESPA), which can identify user stress accurately by exploring the corresponding rules between emotion and stress. We constructed a stress emotion dictionary, mined the pattern rules of emotion sequence under different stress scenarios, and established a stress emotion rule set, aiming at identifying and understanding stress more accurately. The experimental results show that this model achieved a higher accuracy rate in stress identification.