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 that the multi-feature fusion (WOOSD-CNN) word vector model achieves notable improvements in sentiment polarity accuracy and categorization effectiveness. Additionally, for aspect-level sentiment analysis of user generated content (UGC) text, a unified learning framework based on an information interaction channel is proposed, which enables the team productivity center (TPC) task. Specifically, an information interaction channel is designed to assist the model in leveraging the latent interactive characteristics of text. An in-depth analysis addresses the label drift phenomenon between aspect term words, and a position-aware module is constructed to mitigate the local development plan (LDP) issue.