How does consumer demand for new energy vehicles drive innovation? -- based on social network and text mining analysis
Bibliographic record
Abstract
Abstract Market demand is the internal source of technological innovation. Automobile enterprises get various information about product improvement from consumers, and consumer demand points out the direction of technological innovation for new energy automobile enterprises. First, Python was used to write a crawler to crawl the text data of consumer comments on new energy vehicles on Autohome website. Then, big data analysis is carried out based on social network, word cloud map, LDA topic model and other methods. Finally, the empirical study found that commuting, picking up children, appearance, space, appearance level, and interior are among the key factors affecting ordinary consumers to buy new energy vehicles; consumers are not satisfied with the new energy vehicles mainly for the trunk, abnormal sound, endurance, noise, and interior. Among the 8 themes of the LDA theme model, theme 6 (noise problem), theme 1 (brake problem) and theme 3 (odor problem) show high theme intensity, and mass consumers are increasingly dissatisfied with the low financial incentives and high prices of new energy vehicles. Finally, based on the findings of the above influencing factors, relevant management suggestions on product innovation are provided for new energy vehicle enterprises.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".