LAMBDA: Lexicon and Aspect-Based Multimodal Data Analysis of Tweet
Bibliographic record
Abstract
Sentiment analysis (SA) is widely employed across various domains, including government policy directives, corporate customer and staff satisfaction monitoring, political analysis, and public tension monitoring in security structures.However, new difficulties for sentiment analysis algorithms have emerged with the advent of highly unstructured manifestations of emotion in online social media platforms.To address this, we propose an enhanced approach that combines lexicon-based analysis and aspect-based sentiment analysis for tweets.In this study pre-processing step allows us to handle the lack of syntactic and grammatical structure commonly found in social media text.Moreover, acknowledging the multimodal characteristics of social media data, encompassing audio, visuals, and videos, our methodology expands to encompass sentiment analysis of text derived from these diverse modalities.Our approach classifies the content as positive, negative, or neutral, utilizing both the lexicon-based approach and the aspect-based sentiment analysis.By combining these techniques, we aim to capture both general sentiment tendencies and aspect-specific sentiments within the text data.We evaluate the efficacy of our proposed approach using the STS-Gold datasets.Text data achieved the highest sentiment accuracy at 92.83%, followed by audio, image, and video data with accuracies of 91.11%, 87.8%, and 86.67%, respectively by using LAMBDA approach.The results highlight the effectiveness of our approach compared to other state-of-the-art research studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".