Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms
Bibliographic record
Abstract
Every day, a massive amount of text, audio, and video data is published on websites all over the world. This valuable data can be used to gauge global trends and public perceptions. Companies are showcasing their preferred advertisements to consumers based on their online behavioral trends. Carefully analyzing this raw data to uncover useful patterns is indeed a challenging task, even more so for a resource-constrained language such as Urdu. A unique Urdu language-based multimodal dataset containing 1372 expressions has been presented in this paper as a first step to address the challenge to reveal useful patterns. Secondly, we have also presented a novel framework for multimodal sentiment analysis (MSA) that incorporates acoustic, visual, and textual responses to detect context-aware sentiments. Furthermore, we have used both decision-level and feature-level fusion methods to improve sentiment polarity prediction. The experimental results demonstrated that integration of multimodal features improves the polarity detection capability of the proposed algorithm from 84.32% (with unimodal features) to 95.35% (with multimodal features).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".