The Problem
TikTok comment sections contain product feedback, feature requests, and competitive intelligence. Brands manually browse comments on a few videos but miss signals across hundreds of relevant videos.
The Scavio Solution
Search TikTok for brand mentions, extract comments from high-engagement videos, and classify comments by signal type: positive feedback, complaints, feature requests, competitor mentions. Cost: $0.05-0.10 per video analyzed.
Before
Before automated comment analysis, a product team manually read comments on 5-10 TikTok videos per week about their product. They missed a recurring feature request mentioned in 30+ comments across 8 different videos.
After
After implementing comment extraction, the team analyzes 50 videos weekly. The pipeline extracts 500+ comments, classifies them by signal type, and surfaces the top feature requests. The recurring request is detected and prioritized. Weekly cost: $2.50.
Who It Is For
Product managers, brand managers, customer success teams, and competitive intelligence analysts monitoring TikTok feedback.
Key Benefits
- Analyze 50+ TikTok videos per week for $2.50
- Classify comments into feedback, complaints, feature requests
- Detect recurring themes across multiple videos
- Surface competitor mentions in comment discussions
- Quantify sentiment distribution per video
Python Example
import requests, os
H = {'Authorization': f'Bearer {os.environ["SCAVIO_API_KEY"]}', 'Content-Type': 'application/json'}
def extract_signals(brand, pages=2):
vids = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
headers=H, json={'keyword': brand, 'count': 20}).json()
signals = []
for v in vids.get('data', {}).get('videos', [])[:10]:
if v['stats']['commentCount'] > 5:
comments = requests.post('https://api.scavio.dev/api/v1/tiktok/video/comments',
headers=H, json={'aweme_id': v['id'], 'count': 20, 'cursor': 0}).json()
for c in comments.get('data', {}).get('comments', []):
signals.append({'video': v['desc'][:40], 'comment': c['text'],
'likes': c['digg_count']})
return signals
signals = extract_signals('mybrand')
print(f'{len(signals)} comment signals extracted')JavaScript Example
const H = {'Authorization': `Bearer ${process.env.SCAVIO_API_KEY}`, 'Content-Type': 'application/json'};
async function extractSignals(brand) {
const vids = await fetch('https://api.scavio.dev/api/v1/tiktok/search/videos', {
method: 'POST', headers: H, body: JSON.stringify({keyword: brand, count: 20})
}).then(r => r.json());
const signals = [];
for (const v of (vids.data?.videos || []).slice(0, 10)) {
if (v.stats.commentCount > 5) {
const c = await fetch('https://api.scavio.dev/api/v1/tiktok/video/comments', {
method: 'POST', headers: H, body: JSON.stringify({aweme_id: v.id, count: 20, cursor: 0})
}).then(r => r.json());
(c.data?.comments || []).forEach(cm => signals.push({comment: cm.text, likes: cm.digg_count}));
}
}
console.log(`${signals.length} signals`);
}
extractSignals('mybrand');Platforms Used
TikTok
Trending video, creator, and product discovery