How Does Lululemon Shape Its Brand Loyalty Through Influencing Public Perception of Healthy Female Body Aesthetics on YouTube?
Bibliographic record
Abstract
In modern marketing, social media and brand marketing have gradually established an inseparable relationship. This study aims to discuss how brands shape public aesthetics and build brand loyalty through social media. For this reason, this work has diversified research methods. Advanced machine learning algorithm was applied to deeply dig the audience comments of Lululemon's advertising videos on YouTube and analyze the emotional tendency of the collected comments with the help of natural language processing. This work also conducted content analysis on Lululemon's most popular advertising videos on YouTube as a sample. The results show that excellent video content not only increases the stickiness bet these woken brands and consumer, but also improves consumers' purchase intentions. These results show that in the modern market, brands should pay more attention to the quality of social media content to enhance brand loyalty, and they should also be able to obtain consumer needs from user comments, which provides an important sentence for further improving marketing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".