Sentiment Analysis Using Smoothed Probabilistic-Based Models
Bibliographic record
Abstract
In this paper, we propose an unsupervised learning algorithm for Natural Language Processing (NLP). In particular, we present a sentiment analysis solution using probabilistic models. We propose two new smoothed probabilistic-based models that incorporate the benefits of smoothing techniques and scaling word vectors to address sparseness and high-dimensionality challenges. We introduce the smoothed scaled Dirichlet and the smoothed shifted scaled Dirichlet mixture models, the learning approach for the mixture parameters, the clustering algorithms, and the sentiment analysis framework. We consider in our experiments different benchmarks of sentiment analysis, namely, Stanford Twitter sentiment (STD), Stanford sentiment gold standard (STS-Gold), SemEval2014 Task9, and Sentiment Strength Twitter (SentiStrength). The results are compared with the baselines and state-of-the-art (SOTA) models in the literature where our proposed approaches outperform all the other models.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".