Question: What emotional vocabulary does oil-control shampoo produce beyond "good" and "bad"?
Data: 269 raw emotion words extracted from emotional_shape.primary_words across 14 blogger posts (134 products). After filtering out 26 evaluative-only entries ("好用", "不推荐") per CLAUDE.md Rule 4, 243 genuine emotion entries remain.
Classification: Each entry was tagged on three axes: valence (positive / negative / ambivalent), arousal (high / medium / low), and emotion type (19 types). 102 emotional contradictions were also captured from emotional_shape.contradictions.
The near-perfect symmetry says something specific about oil-control shampoo. Consumers arrive with a felt pain point (oily scalp) and look for sensory cues that confirm whether the product works. They are not casually browsing. The result is binary: body-feel cues either confirm efficacy (positive) or fail to do so (negative). Few consumers land in the ambivalent middle.
This connects directly to S14 (Somatic Black Box). The "next morning scalp check" is when the verdict forms. If scalp still feels fresh, the emotion turns positive. If oil returns visibly overnight, the emotion turns negative. The body delivers a binary signal, and the emotion follows.
For PDP: do not try to create positive emotion with promotional language. Provide the sensory cues that trigger the positive verdict. The emotion follows the body-feel, not the claim.
19 emotion types, sorted by frequency. Bar color shows valence: green = positive, red = negative, amber = ambivalent.
Arousal is easier to read as a temperature ranking. Low=1, Medium=2, High=3. The marker shows the average arousal score for each emotion type.
How to read it: use the ranking as the evidence, then use these cards as the interpretation layer.
Anger is 100% high-arousal. Fear and disgust are mostly high-arousal. When oil-control fails, consumers do not simply dislike it; they escalate.
Satisfaction is mostly low-arousal, and relief is almost entirely low. When a product works, the emotional reward is calm trust, not constant excitement.
Surprise is 84% high-arousal. The positive peak comes from exceeding expectations, not from normal product satisfaction.
Anger is 100% high-arousal. Disgust and fear are mostly high. Satisfaction is 67% low-arousal, and trust spreads across all levels. The exception is surprise (惊喜): 84% high-arousal. The positive peak in this category comes from exceeding expectations, not from sustained pleasure. When a product works, the feeling is quiet relief and steady trust. When it fails, the feeling is loud contempt and active anger.
For PDP: do not over-promise. Quiet positives (trust, satisfaction, relief) are what consumers feel when a product works. Loud positives (surprise, delight) happen when expectations were low. Over-promising raises the bar and can turn satisfaction into disappointment.
Of the 14 KS emotion entries in the dataset, 12 come from a single blogger (一只阿痞) who tested 4 of the 5 KS product lines reviewed. The negative skew in this section reflects one individual's emotional trajectory across the KS range, not population-level consumer sentiment. Findings below should be read as a single-source signal, not as a representative sample.
Across 5 KS product lines (元气姜, 白金, 双重净油, 元气浆, 黑钻), the overall emotion profile skews 64% negative. However, this negativity concentrates in specific sub-lines — notably 元气姜 (Ginger Recovery) and 白金 (Platinum) — and is driven almost entirely by one blogger's experience. The single KS product reviewed by a different blogger (冬瓜皮薇薇 reviewing 元气浆) was positive. KS does not appear to face broad brand-level rejection; the signal is product-line-specific and single-source.
PR note: The blogger 一只阿痞 demonstrates a high degree of antipathy toward several KS product lines, expressing emotions ranging from contempt to price-betrayal anger across multiple posts. This individual may warrant monitoring from a brand reputation and PR perspective, as their content carries strong emotional conviction and reaches an engaged audience. However, the concentrated single-source nature of this data means it should not be treated as evidence of widespread consumer sentiment.
An emotion arc traces how a consumer's feeling moves from start to finish of their product experience — from first impression through daily use to final verdict. Think of it as the emotional plot line of a product encounter. Each arc below is drawn from real blogger language and follows the path: expectation → body-feel test → emotional outcome.
Why this matters for marketing: These arcs reveal the emotional stories your product will trigger in consumers. By understanding which arc a product is likely to follow, marketing teams can (1) anticipate the narrative consumers will tell about the product, (2) design communications that amplify a positive arc or preempt a negative one, and (3) identify which body-feel moments are the emotional turning points — the moments where messaging can intervene.
How to use them: Match your product's profile to the arc patterns below. If your product delivers strong immediate results but may fade over time, you are likely in Arc 5 (Inverted V) territory — and your messaging should set expectations for the long-term experience. If your product is a quiet performer, Arc 9 (The Quiet Positive) shows the emotional language your satisfied consumers will use: not fireworks, but "the numbers check out."
Emotions in this category do not appear alone. They travel in arcs. Each arc traces one blogger's emotional trajectory with one product, showing how body-feel triggers cascade into specific emotional shapes.
The negative arcs (1–3) are long and multi-stage. They have tension, climax, and resolution. Bloggers perform negative emotion, turning disappointment into content. The positive arcs (7–9) are shorter and quieter. Trust is confirmed, not narrated. Consumers will remember the betrayal stories. Positive stories need a surprise element, like Arc 8's 折服, to travel as a plot.
An emotional contradiction is when a consumer holds two opposing feelings about the same product at the same time: "I love the wash-feel but hate the oil control." "It worked at first but got worse over time." These are not judgment errors. They are some of the clearest emotional data in the dataset.
Contradictions show the trade-off structure of the category: what consumers give up in exchange for what they get. A product with zero contradictions is either perfect (rare) or under-described (common). A product with many contradictions is one the consumer has lived with long enough to see its full shape.
102 contradictions across 243 emotion entries = 42% contradiction rate. Nearly half of all emotional responses come with a "but."
The brand says X, the body says Y. This is the largest category because consumers arrive with PDP expectations, then run a body-feel test that either confirms or falsifies the claim.
The product excels at one dimension but fails at another. These contradictions reveal that oil-control shampoo has multiple performance axes that rarely align. Consumers want all of them; products deliver some at the cost of others.
The brand's reputation or other products set expectations that this specific product betrays. The emotional intensity is highest here because trust was extended first.
The product starts well, then degrades. This connects to S14's monitoring pattern: the emotional arc mirrors the hair's physical arc (initial lift → gradual oil return → collapse).
Oil-control shampoo is not simply a "works or doesn't" product. Gains on one axis (oil control) often cost another (dryness, volume loss, scent). Consumers know this. They do not expect perfection. They expect the trade-off to be acknowledged.
When PDP claims "oil control + volume + gentle" without naming any cost, the consumer's body-feel test finds the cost. The contradiction is not only in the product. It sits between what PDP promised and what the body discovered. PDP that names the trade-off upfront ("thorough clean; pair with conditioner for ends") can reduce the betrayal feeling.
A consumer who says "volume is great but oil control is weak" gives actionable feedback. They identify which axis works and which does not. PDP can use this pattern: lead with the working axis, acknowledge the weaker axis, and suggest a solution (e.g., "pairs with our scalp serum for extended oil control").
Products that start well and degrade over time produce deep disappointment because the consumer trusted the initial signal. This connects to S14: the immediate-to-next_day comparison is the highest-stakes moment. Time-decay contradictions add a second cliff: the product works for weeks, then stops working. PDP should address long-term performance, not just the first wash.