Abstract: With the rise of social media and online review platforms,automated sentiment analysis has become a key tool for understanding public emotions,consumer preferences,and market trends.Traditional sentiment analysis methods often use classification models that focus on extracting the overall sentiment of the text,neglecting the complex and multidimensional emotional information that may be contained within the comments.Addressing this issue,this study proposes a multi-granularity text-based sentiment analysis model using generative models to intricately capture aspect-level and document-level emotions in review texts.Additionally,a structured output format is constructed that includes sentiment labels for different aspects of the review text as well as the overall sentiment label of the review text.Compared to traditional classification models,the proposed model more comprehensively understands and reflects the emotional structure of text,achieving extraction and classification of multifaceted emotional information and overall sentiment in comments.Experimental results show that the proposed modelis better than conventional classification methods in the recognition of overall emotions and aspect emotions,and achieves a 4.4% higher F1-Score than the Bert+LSTM model.

Key words: Natural language processing,Text generation,Structured output,Multi-grained,Review sentiment analysis