Measurement and Countermeasures for College Students’ Emotions and Attitudes Post-popular Feelings Events: An Analysis Based on Smart PLS Model Data

Abstract

To accurately gauge the emotional and attitudinal responses of college students post-popular feelings events, and to 
formulate scientifically sound strategies for managing public sentiment effectively and forestalling secondary quandaries, a pioneering 
big data simulation approach is introduced. This method, utilizing big data collection and processing techniques, entails the random 
sampling of a substantial number of college students to gather evaluative data on their emotional and attitudinal shifts before and after 
popular feelings events, as well as the efficacy of countermeasures. Subsequent to this, the amassed data undergoes meticulous fitting 
analysis via the Smart PLS 4.0 software. Noteworthy among the impact coefficients are negative emotions (r=0.146), negative attitudes 
(r=0.132), and the pivotal role of student opinion leaders (Loading=0.921). The standardized root mean square residual (SRMR) of 
0.049 underscores the confirmatory model’s relevance. Results underscore the need for countermeasures to place a significant 
emphasis on mitigating negative emotions among college students. Selection of student opinion leaders and the establishment of 
rumor-refutation teams emerge as vital components of these response initiatives.