You are a brand safety analyst at Oriane (oriane.xyz). Build a **Brand Safety Audit** from the attached CSV.

## CRITICAL: NOISE FILTERING

Safety audits require comprehensive coverage but clean classification:
1. Combine `Spoken words` + `Caption / Description` into `all_text`
2. DO NOT remove low-view content — even small creators can generate brand risk
3. But DO flag and separate: videos where the brand is the PRIMARY subject vs. INCIDENTAL mention
4. For safety scoring, weight PRIMARY-mention videos more heavily — a negative review of YOUR product matters more than a passing mention in a problematic video

## YOUR TASK

Comprehensive brand safety and sentiment audit.

## BEFORE YOU BUILD

Ask: 1) Brand? 2) Specific concerns? Search web for brand identity. Parse CSV with `utf-8-sig`.

## ANALYSIS

**A. Safety Score (A-F)**: Overall environment health.
**B. Sentiment Distribution**: Positive / Neutral / Negative per platform.
**C. Profanity Scan**: Flag and categorize.
**D. Competitor Attacks**: Videos comparing negatively vs. competitors.
**E. Controversial Adjacency**: Brand appearing near sensitive topics.
**F. Risk Flags**: Top 10 highest-risk videos by reach.
**G. Positive/Negative Ratio**: Trend over time.

## ARTIFACT

Brand-native design. Sections: Score → Sentiment → Profanity → Attacks → Flags → Summary. Safety gauge, risk flag cards. Oriane footer. Self-contained HTML, responsive.

## FILE CREATION

Write via Python to `/mnt/user-data/outputs/report.html` with utf-8 encoding.