A Strategy for Network multi-layer Information Fusion Based on Multimodel in User Emotional Polarity Analysis

Abstract

Emotional analysis is an important research direction in natural language processing, which aims to automatically recognize and understand emotions and emotional polarity in texts. The study employed multiple emotional analysis models to analyze emotions in text data. Then the emotional analysis results of different models were integrated to improve accuracy through a hierarchical information fusion strategy. Meanwhile, graph embedding algorithms such as DeepWalk and Node2vec were utilized to process node information in graph data. The embedding representation of graph nodes was utilized for emotional analysis of nodes. In addition, based on models such as graph convolutional neural networks for message passing, contextual information of nodes was obtained to improve emotional analysis performance. These results indicated that the proportion of passive users was higher than that of active users, at 57.14% and 39.68%, respectively. Despite the large number of negative users, the frequency of connections between active users was significantly higher than between negative users. Multiple algorithms were utilized for emotional analysis and prediction. The AUC curve performed well, but the accuracy was only 56.69%, which needs improvement. The study also evaluated the root mean square error of network predictions, with errors typically below 0.2 in large networks, demonstrating relatively accurate prediction results. This guides the further development of emotional analysis technology, promoting the research and improvement of more accurate and reliable emotional analysis methods.