Sentiment Analysis Models with Bayesian Approach: A Bike Preference Application in Metropolitan Cities
Bibliographic record
Abstract
Social media data is an important source of information that can also be used for the study of the passenger mobility sector. In transport systems, user choice is studied through demand models that define how user behavior is affected by the performance of the supply system. Demand models are typically calibrated through data observed in the transport system. The observed data includes the choices actually made by users. This paper investigates how sentiment analysis of data available in social media can be adopted to specify, calibrate, and validate demand models in certain choice levels. In this work a model based on the Bayesian approach is specified, calibrated, and validated in the case of bike preference in some Italian metropolitan cities. The model takes into account the discrete choice approach. Specification, calibration, and validation made it possible to identify the relevant variables that influence sentiments and obtain the posterior distribution probability of the parameters. The prior and the posterior conditional probabilities are compared, and some indications are obtained on the elasticity and weight of the sentiments that influence the choice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".