Quantitative insights from online qualitative data: An example from the health care sector
Bibliographic record
Abstract
Abstract Among the deluge of online data generated by users in the form of text on social media sites, health care reviews are among the most common, and potentially, the most insightful. Patients review and comment on the experiences with procedures as varied as hysterectomies, colonoscopies, and chemotherapy. In their attempts to reduce the uncertainty associated with medical treatments, many patients nowadays also turn to social media, where they rely on the experiences articulated by other patients. In this study, IBM Watson is used to examine how knee replacement patients talk about their emotions and express sentiment through their comments online. Then, a latent class cluster modeling procedure is used to segment these patients into distinct groups, according to their emotions (anger, disgust, fear, happiness, sadness, and surprise), sentiment, and their overall satisfaction with knee replacement surgery. The findings show how qualitative online data can be transformed into quantitative insights regarding underlying market segments, which could then be targeted through different strategies by both marketers and health care practitioners.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".