Analyzing and Recommending Network User Behavior Using Natural Language Processing

Abstract

With the rapid development and popularization of network technology, the internet has become an indispensable part of people's daily lives. At the same time, the analysis and recommendation of online user behavior have become important links in the development of the internet. This study proposes a unified framework that integrates fine-grained sentiment analysis and an Attention-Guided recommendation Algorithm (AGA), enhances model robustness through FreeLB adversarial training, and dynamically captures user-item associations using a collaborative attention mechanism. This study focuses on its potential application in engineering management and project operation, such as analyzing team behavior and feedback from textual data, such as equipment logs and engineering reports, in order to provide intelligent support for optimizing project management decisions, resource allocation, and risk response. The experimental results show that using the FreeLB model can improve the accuracy of fine-grained sentiment analysis and increase the model's recall rate for positive and negative samples. Compared to other models, the recommendation model's loss decreased by 3.6% and 5.2%, respectively. The results indicate that the model can still maintain high prediction accuracy when processing data from different fields. The research results provide strong support for the analysis and recommendation of online user behavior.