Fine-Grained Sentiment Quantification of Media Texts Considering Sentence Type and Holder–Target Awareness

Media texts convey emotions and stances that can shape the evolution of public opinion, which calls for comparable quantitative sentiment analysis. However, most existing approaches assign a single sentiment score to an entire article, making it difficult to distinguish functional differences across sentences and to clarify who expresses the sentiment and what the evaluation targets, thereby limiting interpretability and cross-source comparability. To address this issue, we propose a fine-grained sentiment quantification method for media texts that jointly considers sentence types and opinion holder–target structure. The method obtains sentence-level sentiment scores and simultaneously extracts sentence types, opinion holders, and opinion targets, enabling article-level structured quantification and comparison under a unified evaluation setting. In our implementation, a large language model (LLM) is primarily used for semantic parsing and structured extraction. Experiments demonstrate that the proposed method delivers stable performance on the sentiment score regression task (R2 = 0.899, MAE = 0.088, MSE = 0.027; relative to the strongest fine-tuned pretrained language model baseline in our comparison, RoBERTa with R2 = 0.871, this corresponds to a 2.8-percentage-point gain in R2 and an 8.3% reduction in MAE), and effectively supports opinion holder–target identification (holder weighted average F1 = 0.812; target loose F1 = 0.691 in a supplementary evaluation). Building on these outputs, the method can further reveal the spatial distribution of sentiment bias in global media coverage, highlighting relative sentiment patterns in cross-national narratives.