Constructing business profiles based on keyword patterns on Web sites
Bibliographic record
Abstract
Abstract The study examined the possibility of constructing business profiles (specifically, product profiles) based on keyword patterns on various types of Web sites, including a company's own Web site, blog sites, and Web sites that have particular keywords and also hyperlinks pointing to company Web sites. To test the proposed methods, we selected China's four major oil companies and two other companies that have related products. We collected three rounds of data over a 7‐month period from these three Web sources and analyzed the numbers of retrieved pages to construct business profiles. The business profiles constructed were checked against business information collected from other sources such as company annual reports and company newsletters to determine the correctness of the profiles and thus the usefulness of the proposed methods. We found that we can construct fairly accurate profiles by examining the frequency distribution of product keywords on company Web sites. Analyzing the frequency distribution of blogs on various topics was very useful in following major business events and developments during particular time periods. We also conducted qualitative content analysis for a sample of 454 Web pages retrieved from the three sources. Findings from the content analysis confirmed the conclusions from the quantitative analysis.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".