A study of the possibilities of text mining and machine learning for score evaluation and review content
Bibliographic record
Abstract
With the widespread use of the Internet, there are more and more opportunities to purchase a variety of products through online shopping. The opportunities are not only for small products such as books, but also for home appliances. Previously, when purchasing a product, users who wanted to buy a product would visit a store and get expert advice on what to buy. Now, however, customers consider reviews on the Internet to be more important information for considering the products to be purchased. And evaluation page consists of an overall evaluation, an evaluation of each feature, and comments, which are word of mouth. The overall evaluation and the evaluation of each feature is often a score evaluation, and organized information such as the average and the distribution of scores are presented. However, it is difficult to read all the comments that are word of mouth because they are often enumerated as is. Therefore, in this study, we created a system to label which features people commented on in response to the word of mouth comments using data from the TV’s comprehensive evaluation page. 2392 TV evaluation results from Sony.com were used. From the extracted data, text mining was performed on the comments, which are word of mouth, followed by labels of which features are commented on. When 80\% of the test data was prepared and implemented against 20\% of the learning data, the label was predicted with 77\% accuracy. From this study, we used text mining to label the comments, which are customer impression. from the current study, text mining was used to label the comments, which are customer impression. The results and score ratings were used to identify customer trends.
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 | 0.046 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".