Multi-source Heterogeneous Public Opinion Analysis via Collaborative Reasoning and Adaptive Fusion: A Systematically Integrated Approach
arXiv:2602.15857v1 Announce Type: new Abstract: The analysis of public opinion from multiple heterogeneous sources presents significant challenges due to structural differences, semantic variations, and platform-specific biases. This paper introduces a novel Collaborative Reasoning and Adaptive Fusion (CRAF) framework that systematically integrates traditional feature-based methods with large language models (LLMs) through a structured multi-stage reasoning mechanism. Our approach features four key innovations: (1) a cross-platform collaborative attention module that aligns semantic representations while preserving source-specific characteristics, (2) a hierarchical adaptive fusion mechanism that dynamically weights features based on both data quality and task requirements, (3) a joint optimization strategy that simultaneously learns topic representations and sentiment distributions through shared latent spaces, and (4) a novel multimodal extraction capability that processes video con
arXiv:2602.15857v1 Announce Type: new Abstract: The analysis of public opinion from multiple heterogeneous sources presents significant challenges due to structural differences, semantic variations, and platform-specific biases. This paper introduces a novel Collaborative Reasoning and Adaptive Fusion (CRAF) framework that systematically integrates traditional feature-based methods with large language models (LLMs) through a structured multi-stage reasoning mechanism. Our approach features four key innovations: (1) a cross-platform collaborative attention module that aligns semantic representations while preserving source-specific characteristics, (2) a hierarchical adaptive fusion mechanism that dynamically weights features based on both data quality and task requirements, (3) a joint optimization strategy that simultaneously learns topic representations and sentiment distributions through shared latent spaces, and (4) a novel multimodal extraction capability that processes video content from platforms like Douyin and Kuaishou by integrating OCR, ASR, and visual sentiment analysis. Theoretical analysis demonstrates that CRAF achieves a tighter generalization bound with a reduction of O(sqrt(d log K / m)) compared to independent source modeling, where d is feature dimensionality, K is the number of sources, and m is sample size. Comprehensive experiments on three multi-platform datasets (Weibo-12, CrossPlatform-15, NewsForum-8) show that CRAF achieves an average topic clustering ARI of 0.76 (4.1% improvement over best baseline) and sentiment analysis F1-score of 0.84 (3.8% improvement). The framework exhibits strong cross-platform adaptability, reducing the labeled data requirement for new platforms by 75%.
Executive Summary
The article introduces the Collaborative Reasoning and Adaptive Fusion (CRAF) framework, a novel approach to analyzing public opinion from multiple heterogeneous sources. CRAF integrates traditional feature-based methods with large language models (LLMs) through a structured multi-stage reasoning mechanism. The framework includes a cross-platform collaborative attention module, a hierarchical adaptive fusion mechanism, a joint optimization strategy, and a multimodal extraction capability. Theoretical analysis and experiments demonstrate significant improvements in topic clustering and sentiment analysis, with strong cross-platform adaptability.
Key Points
- ▸ Integration of traditional feature-based methods with LLMs
- ▸ Cross-platform collaborative attention module
- ▸ Hierarchical adaptive fusion mechanism
- ▸ Joint optimization strategy for topic and sentiment analysis
- ▸ Multimodal extraction capability for video content
Merits
Innovative Framework
The CRAF framework is a significant advancement in the field of public opinion analysis, offering a systematic integration of diverse methodologies.
Theoretical Rigor
The theoretical analysis provides a strong foundation, demonstrating a tighter generalization bound compared to independent source modeling.
Empirical Success
Comprehensive experiments show substantial improvements in topic clustering and sentiment analysis, with strong cross-platform adaptability.
Demerits
Complexity
The framework's complexity may pose challenges in implementation and scalability, particularly for organizations with limited resources.
Data Dependency
The effectiveness of CRAF is highly dependent on the quality and availability of data from multiple sources, which may not always be consistent or reliable.
Generalizability
While the framework shows promise, its generalizability to other domains or contexts beyond the studied platforms remains to be thoroughly tested.
Expert Commentary
The CRAF framework represents a significant leap forward in the field of public opinion analysis. By integrating traditional feature-based methods with the latest advancements in large language models, the authors have created a robust and adaptable system. The theoretical analysis is particularly noteworthy, as it provides a solid foundation for the empirical results. The framework's ability to handle multimodal data, including video content, is a notable innovation that expands the scope of public opinion analysis. However, the complexity of the framework and its dependency on high-quality data are potential challenges that need to be addressed. Additionally, the generalizability of the framework to other domains remains an open question. Overall, the CRAF framework is a promising development that has the potential to significantly enhance our understanding of public opinion across diverse platforms.
Recommendations
- ✓ Further research should focus on simplifying the implementation of the CRAF framework to make it more accessible to a broader range of users.
- ✓ Efforts should be made to address potential biases in the data and models to ensure fair and accurate analysis.
