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Multi-source Heterogeneous Public Opinion Analysis via Collaborative Reasoning and Adaptive Fusion: A Systematically Integrated Approach

Yi Liu

TL;DR

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 that exhibits strong cross-platform adaptability.

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%.

Multi-source Heterogeneous Public Opinion Analysis via Collaborative Reasoning and Adaptive Fusion: A Systematically Integrated Approach

TL;DR

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 that exhibits strong cross-platform adaptability.

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%.
Paper Structure (41 sections, 3 theorems, 15 equations, 5 figures, 6 tables)

This paper contains 41 sections, 3 theorems, 15 equations, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background and Significance
  3. Related Work and Limitations
  4. Research Contributions
  5. Paper Organization
  6. Problem Formulation and Theoretical Foundations
  7. Formal Problem Statement
  8. Feature Representation Spaces
  9. Theoretical Analysis of Multi-source Fusion
  10. Collaborative Reasoning and Adaptive Fusion Architecture
  11. Overall Architecture
  12. Source-Specific Encoding
  13. Collaborative Attention Module
  14. Adaptive Fusion Layer
  15. Hierarchical Feature Refinement
  16. ...and 26 more sections

Key Result

Theorem 2.1

For $K$ sources with $m$ samples each and $d$-dimensional features, the excess risk $\mathcal{E}$ of CRAF compared to the optimal hypothesis $h^*$ is bounded by: where $\hat{h}_{\text{CRAF}}$ is the empirical risk minimizer for CRAF and $\hat{h}_k$ are the minimizers for independent source models.

Figures (5)

  • Figure 1: Architecture of the Collaborative Reasoning and Adaptive Fusion (CRAF) framework. The system processes multi-source heterogeneous data through four main stages: (1) source-specific encoding, (2) cross-platform collaborative attention, (3) adaptive feature fusion, and (4) multi-task analysis.
  • Figure 2: Attention patterns learned by the collaborative attention module. Each bar group represents attention weights from one source platform to all platforms, demonstrating cross-platform information flow. Note the diagonal dominance indicating source identity preservation, with off-diagonal weights enabling cross-platform information exchange.
  • Figure 3: Cross-platform consistency heatmap (Jaccard similarity). Darker colors indicate higher consistency. CRAF achieves the highest consistency scores across all platform pairs.
  • Figure 4: Few-shot adaptation performance on new, unseen platforms. CRAF achieves target performance with only 50 labeled samples, compared to 200 samples required by BERT fine-tuning.
  • Figure 5: Real-time sentiment evolution during a major product launch event monitored by the CRAF system.

Theorems & Definitions (5)

  • Theorem 2.1
  • proof : Proof Sketch
  • Corollary 2.2
  • Theorem 3.1: Refinement Approximation
  • proof : Proof Sketch