Multimodal Sentiment Analysis: A Multitask Learning Approach
Bibliographic record
Abstract
Multimodal sentiment analysis has recently received an increasing interest. However, most methods have considered that text and image modalities are always available at test time. This assumption is often violated in real environments (e.g. social media) since users do not always publish a text with an image. In this paper we propose a method based on a multitask framework to combine multimodal information when it is available, while being able to handle the cases where a modality is missing. Our model contains one classifier for analyzing the text, another for analyzing the image, and another performing the prediction by fusing both modalities. In addition to offer a solution to the problem of a missing modality, our experiments show that this multitask framework improves generalization by acting as a regularization mechanism. We also demonstrate that the model can handle a missing modality at training time, thus being able to be trained with image-only and text-only examples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".