Social media network public opinion emotion classification method based on multi-feature fusion and multi-scale hybrid neural network.

ABSTRACT: With the rapid development of the internet, an increasing number of users express their subjective opinions on social media platforms. By analyzing the sentiment of these texts, we can gain insights into public sentiment, industry changes, and market trends, enabling timely adjustments and preemptive strategies. This article initially constructs vectors using semantic fusion and word order features. Subsequently, it develops a lexicon vector based on word similarity and leverages supervised corpora training to obtain a more pronounced transfer weight vector of sentiment intensity. A multi-feature fused emotional word vector is ultimately formed by concatenating and fusing these weighted transfer vectors. Experimental comparisons on two multi-class microblog comment datasets demonstrate tha

SUBMITTER: Yao Y 

PROVIDER: S-EPMC11784739 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature