Innovation and Application Research of Text Sentiment Analysis Model Based on Attention Mechanism
Ge Youwang

Mongolian National University，Mongolia，16060；

Abstract: Text sentiment analysis, as an important branch of the field of natural language processing, holds key application value in scenarios such as public opinion monitoring, customer feedback analysis, and intelligent recommendation. Traditional text sentiment analysis models often suffer from insufficient analytical accuracy when processing semantically complex texts with strong contextual dependencies, due to their inability to effectively capture key sentiment information. The attention mechanism, with its ability to selectively focus on important features in text, provides a new approach to solving this problem. This paper focuses on research into text sentiment analysis models based on the attention mechanism. It first reviews the research status and limitations of existing models, then proposes innovative directions regarding model structure and feature fusion methods. Finally, it discusses the application value and future development trends of these models in practical application scenarios, aiming to provide theoretical reference and technical insights for enhancing the accuracy and practicality of text sentiment analysis.

Keywords: Attention Mechanism; Natural Language Processing; Model Innovation

References

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