Designing an intelligent push model for user emotional topics based on dynamic text categorization in social media news dissemination.

ABSTRACT: The exploration of social media comment analysis has garnered considerable scholarly attention in recent epochs, precipitated by the pervasive ubiquity of social media platforms and the copious volume of commentaries engendered by their users. As the prevalence of users disseminating opinions, engaging in news discourse, and articulating sentiments on social media escalates, scrutinizing social media comments assumes paramount significance. This treatise employs a sophisticated deep network model for sentiment classification predicated on online social media textual commentary data, utilizing a bidirectional long short-term memory (BI-LSTM) network. The model initiates data input processing by employing word segmentation and word vector extraction, culminating in the formation of an attent

SUBMITTER: Wang J 

PROVIDER: S-EPMC11784813 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature