Research on the Evaluation of E-Commerce Cold Chain Food Consumption Based on Big Data
Bibliographic record
Abstract
In response to the problems of low efficiency and high cost of offline questionnaires, lack of keyword-consumer attitude correlation in online comment analysis, and little help for optimization solutions, an opinion analysis and improvement evaluation model based on sentiment analysis and Latent Dirichlet Allocation (LDA) document topic generation model is proposed. Using cold-chain food as the research object, a custom Python program was used to crawl the online consumer reviews about cold-chain food from Jingdong, a mainstream Chinese e-commerce company. A total of 70,134 reviews were obtained, including 65,535 valid reviews, which were analyzed by the LDA topic model and SnowNLP sentiment analysis to obtain the influencing factors and specific scores that affect consumer satisfaction. Then the importance ranking of the influencing factors was obtained by combining the word frequency and scores. Finally, based on the rankings and the reasons generated, suggestions are made for developing products and services for cold chain food. From theory and practice, it provides a reference basis for developing cold chain technology and consumer behavior research.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".