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With the proliferation of the internet, many social platforms continue to emerge, giving rise to a surge in user-generated content. Consequently, abundant textual information permeates the virtual realm, wielding a certain impact on public opinion orientation. One can discern the prevailing sentiment prevailing in society by scrutinizing the latent emotional undercurrents embedded within vast news texts. This article presents an RBTM model adept at extracting and analyzing emotional information from mass media, thus lending invaluable insights into the evolution of the intelligent news industry. The empirical findings substantiate the RBTM model&#x2019;s preeminence over its counterparts, evinced by its superior training duration and predictive prowess. Notably, the RBTM model efficaciously deciphers emotional inclinations within news content during practical applications, obviating the need for extensive manual inspection while curtailing analysis time by 49% across departmental endeavors. As an outcome, this paper deliberates upon the tantalizing prospects of intelligent news analysis methods contingent upon emotional information extraction, thereby paving the way for a formidable future in media comprehension.

Evaluation of the Development Value of Emotional Data Mining in Mass Media Using the RBTM Model