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.