Making Sense of Online Consumer Reviews: A Methodology
Bibliographic record
Abstract
Online consumer reviews have become an increasingly important source of information for both consumers (i.e. about whether to buy) and marketers (i.e. about product strengths and weaknesses). However, online consumer reviews are unstructured and unsystematic in nature, making interpretation of these reviews an enormous challenge. The current paper sheds light on a particular methodology that can be used to investigate what consumers say about companies, brands or products. Consumer reviews of the four best-selling games available on Apple's App Store were compiled. Leximancer, a content analysis package, was used to compare comments from users who provided games with a five-star rating versus a one-star rating. Results from the Leximancer analysis reveal the most common themes and concepts that consumers use to describe their experience with these games. Specifically, five-star reviewers describe games as fun, awesome, amazing and addictive; one-star reviewers describe games as boring, easy and stupid. Additionally, negative reviews include themes regarding the presence of ads, technological difficulties and value. Future research should explore how consumers and marketers use this information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.129 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".