The Application of Deep Learning in Text Content Mining and Sentiment Analysis

With the exponential growth of global data volume in the digital era and the increasing proportion of unstructured datasuch as user reviews, industry reports, and social media postsenterprises and institutions are in urgent need of filtering reliable decision-making bases from massive text content to drive strategic planning and operational optimization. Traditional manual processing methods, however, are no longer viable due to their low efficiency, high labor costs, and susceptibility to subjective biases, especially when facing data scales of millions or even billions. Breakthroughs in natural language processing (NLP) technology have significantly enhanced machines ability to understand text semantics, moving beyond simple keyword recognition to grasp contextual logic and implicit meaning. Meanwhile, the integration of multi-source data (e.g., text, user behavior, and temporal-spatial data) with text mining has broken information silos, enabling more comprehensive and contextualized content analysis. This thesis mainly studies the role of deep learning in text content mining and sentiment analysis. It finds that innovative designsincluding deep learning models like BERT and graph neural networks, as well as hierarchical attention mechanismshave notably strengthened semantic understanding and emotion capture capabilities. Additionally, the fine-tuning of pre-trained models has opened up new avenues for tailored applications in specific fields such as healthcare and finance.