动态Emoji感知与双向跨模态注意力大模型情感分析OA

Social media platforms(such as Twitter)serve as vital conduits for user sentiment and public discourse in modern society.Their vast volumes of user-generated content carry rich personal emotions and collective opinions.The real-time nature,short-text characteristics,diversity,and unstructured nature of this data provide a rich foundation for sentiment analysis research while also presenting significant technical challenges.Traditional sentiment analysis methods based on a single text modality struggle to fully capture the complex emotional information embedded in social media.They particularly overlook the significant emotional characteristics conveyed by visual modalities(e.g.,images)and symbolic modalities(e.g.,Emojis),leading to significant bottlenecks in model performance.Addressing the issues of semantic dynamism and text-image heterogeneity in social media,this paper focuses on the Twitter platform and proposes an emotion analysis framework integrating large language models with dynamic Emoji perception and bidirectional cross-modal attention.This paper designs a bidirectional cross-modal attention mechanism incorporating dynamic modality quality assessment.This mechanism adaptively adjusts fusion weights based on the informational quality and reliability of text and images,outperforming simple feature concatenation or fixed-weight attention fusion.This paper also proposes a context-aware dynamic Emoji semantic parsing and text augmentation method driven by large language models.This approach leverages the context-aware capabilities of large language models to overcome the static semantic limitations of Emoji symbols,enhance textual sentiment features,and effectively handle ironic contexts.Utilizing Llama3.1-8B as the base model,combined with advanced low-rank adaptive fine-tuning techniques and hyperparameter optimization strategies,the method achieves high accuracy in sentiment recog-nition while significantly reducing computational resource consumption.Experimental design involves ablation studies validating the effectiveness of multimodal fusion and dynamic Emoji processing.Comprehensive evaluations are conducted on the SemEval-2017 and Twitter-2015/2017 datasets.Experimental results demonstrate improvements across all evaluation metrics for the fine-tuned model.Particularly for samples containing sarcastic content,the model achieves notably higher recognition accuracy due to the complementary nature of multimodal information.