LLM-Guided Multimodal Information Fusion With Hierarchical Spatio-Temporal Graph Network for Sentiment Analysis
Bibliographic record
Abstract
Multimodal sentiment analysis aims to attain a precise comprehension of emotions by integrating complementary textual, visual, and audio information. However, issues such as sentiment discrepancies between modalities, ineffective integration of multi-modal information, and the intricacy of order dependency significantly constrain the models' efficacy. The authors propose an LLM-guided Hierarchical Spatio-Temporal Graph Network (L-HSTGN). By multimodal large model feature enhancement, bidirectional spatio-temporal joint modeling, and dynamic gate fusion mechanism, they effectively address the aforementioned problems. Firstly, they produce cross-modal emotion pseudo-labels based on the multimodal large model, and the single-modal representation was optimized by combining adversarial regularization. Secondly, they develop a bidirectional spatio-temporal convolution module to concurrently extract local-global temporal characteristics and dynamic spatial correlations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".