Aspect-Level Sentiment Analysis Based on a Generalized Probabilistic Topic and Syntax Model
Bibliographic record
Abstract
In this research, we apply a generalized topic and syntax model named Part-of-Speech LDA (POSLDA) to sentiment analysis, and propose several feature selection schemes to separate entities and modifiers so that we can conduct sentiment analysis at both document and aspect levels. We also explore ways of optimizing the model parameters for POSLDA and the training of a classifier based on Maximum Entropy Modeling. The advantage of using POSLDA is that we can automatically separate semantic and syntactic classes, and easily extend it to aspect level sentiment analysis by mapping topics to aspects. However, the noun-related classes, which are also treated as semantic classes, should be removed as much as possible to reduce their impact on sentiment analysis. To evaluate the effectiveness of our solutions, we conducted experiments on two collections of review documents and received the accuracy results competitive to the previous work on sentiment analysis.
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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) | 0.000 | 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".