TikTok social listening catches brand mentions, product discussions, and sentiment shifts that do not appear on any other platform. This pipeline searches TikTok videos for brand keywords, pulls comments from relevant videos, classifies sentiment, and generates a daily digest. Each search and comment pull costs $0.005.
Prerequisites
- Python 3.8+
- requests library
- A Scavio API key from scavio.dev
- Brand terms and keywords to monitor
Walkthrough
Step 1: Search TikTok videos for brand mentions
Find videos that mention your brand or product category.
Python
import os, requests, json
from datetime import datetime
from collections import Counter
API_KEY = os.environ['SCAVIO_API_KEY']
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
BRAND_TERMS = ['scavio', 'serp api', 'search api']
def search_tiktok(query):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
headers=TH, json={'query': query}).json()
videos = data.get('videos', data.get('data', {}).get('videos', []))
return [{'id': v.get('id', ''), 'desc': v.get('desc', '')[:100],
'author': v.get('author', {}).get('uniqueId', 'unknown'),
'plays': v.get('stats', {}).get('playCount', 0),
'likes': v.get('stats', {}).get('diggCount', 0),
'comments': v.get('stats', {}).get('commentCount', 0)} for v in videos]
all_videos = []
for term in BRAND_TERMS:
videos = search_tiktok(term)
all_videos.extend(videos)
print(f' "{term}": {len(videos)} videos found')
print(f'Total: {len(all_videos)} videos. Cost: ${len(BRAND_TERMS) * 0.005:.3f}')Step 2: Pull comments from relevant videos
Fetch comments from high-engagement videos for sentiment analysis.
Python
def get_comments(video_id):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/video/comments',
headers=TH, json={'video_id': video_id}).json()
comments = data.get('comments', data.get('data', {}).get('comments', []))
return [{'text': c.get('text', '')[:200],
'likes': c.get('digg_count', c.get('likes', 0)),
'user': c.get('user', {}).get('uniqueId', c.get('user', {}).get('unique_id', 'anon'))}
for c in comments]
# Get comments from top videos by engagement
top_videos = sorted(all_videos, key=lambda v: v['likes'], reverse=True)[:5]
all_comments = []
for v in top_videos:
if v['id']:
comments = get_comments(v['id'])
all_comments.extend(comments)
print(f' @{v["author"]}: {len(comments)} comments (video: {v["likes"]:,} likes)')
print(f'Total comments: {len(all_comments)}. Cost: ${len(top_videos) * 0.005:.3f}')Step 3: Classify sentiment from comments
Score comments by sentiment to gauge brand perception.
Python
POSITIVE = ['love', 'great', 'amazing', 'best', 'awesome', 'perfect', 'fire', 'goat',
'recommend', 'game changer', 'saved', 'finally']
NEGATIVE = ['hate', 'terrible', 'worst', 'scam', 'trash', 'overrated', 'expensive',
'broken', 'waste', 'disappointed', 'avoid']
def classify_comment(text):
text_lower = text.lower()
pos = sum(1 for w in POSITIVE if w in text_lower)
neg = sum(1 for w in NEGATIVE if w in text_lower)
if pos > neg: return 'positive'
if neg > pos: return 'negative'
return 'neutral'
def sentiment_analysis(comments):
sentiments = Counter()
examples = {'positive': [], 'negative': [], 'neutral': []}
for c in comments:
sentiment = classify_comment(c['text'])
sentiments[sentiment] += 1
if len(examples[sentiment]) < 3:
examples[sentiment].append(c['text'][:80])
total = len(comments)
print(f'\nSentiment Analysis ({total} comments):')
for sent in ['positive', 'negative', 'neutral']:
pct = sentiments[sent] / total * 100 if total else 0
print(f' {sent}: {sentiments[sent]} ({pct:.0f}%)')
for ex in examples[sent][:2]:
print(f' "{ex}"')
return dict(sentiments)
sentiment_analysis(all_comments)Step 4: Generate daily social listening digest
Combine video mentions and comment sentiment into a daily report.
Python
def daily_digest(brand_terms):
print(f'\n=== TikTok Social Listening Digest - {datetime.now().strftime("%Y-%m-%d")} ===')
all_videos = []
cost = 0
for term in brand_terms:
videos = search_tiktok(term)
all_videos.extend(videos)
cost += 0.005
# Deduplicate
seen = set()
unique = [v for v in all_videos if v['id'] not in seen and not seen.add(v['id'])]
print(f'\nVideos found: {len(unique)} (from {len(brand_terms)} searches)')
total_plays = sum(v['plays'] for v in unique)
total_likes = sum(v['likes'] for v in unique)
print(f'Total reach: {total_plays:,} plays, {total_likes:,} likes')
# Top videos
top = sorted(unique, key=lambda v: v['plays'], reverse=True)[:5]
print(f'\nTop mentions:')
for v in top:
print(f' @{v["author"]:20} | {v["plays"]:>10,} plays | {v["desc"][:40]}')
# Comments from top videos
all_comments = []
for v in top[:3]:
if v['id']:
comments = get_comments(v['id'])
all_comments.extend(comments)
cost += 0.005
if all_comments:
sentiment_analysis(all_comments)
print(f'\nDigest cost: ${cost:.3f}')
daily_digest(BRAND_TERMS)Python Example
Python
import os, requests
TH = {'Authorization': f'Bearer {os.environ["SCAVIO_API_KEY"]}', 'Content-Type': 'application/json'}
def listen(brand):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
headers=TH, json={'query': brand}).json()
videos = data.get('videos', data.get('data', {}).get('videos', []))
print(f'{brand}: {len(videos)} TikTok mentions')
for v in videos[:3]:
print(f' @{v.get("author", {}).get("uniqueId", "?")}: {v.get("desc", "")[:40]} ({v.get("stats", {}).get("playCount", 0):,} plays)')
print(f'Cost: $0.005')
listen('serp api')JavaScript Example
JavaScript
const TH = { 'Authorization': `Bearer ${process.env.SCAVIO_API_KEY}`, 'Content-Type': 'application/json' };
async function listen(brand) {
const data = await fetch('https://api.scavio.dev/api/v1/tiktok/search/videos', {
method: 'POST', headers: TH, body: JSON.stringify({ query: brand })
}).then(r => r.json());
const videos = data.videos || data.data?.videos || [];
console.log(`${brand}: ${videos.length} TikTok mentions`);
videos.slice(0, 3).forEach(v =>
console.log(` @${v.author?.uniqueId || '?'}: ${(v.desc || '').slice(0, 40)}`));
}
listen('serp api').catch(console.error);Expected Output
JSON
"scavio": 5 videos found
"serp api": 12 videos found
"search api": 8 videos found
Total: 25 videos. Cost: $0.015
=== TikTok Social Listening Digest - 2026-05-19 ===
Videos found: 20 (from 3 searches)
Total reach: 450,000 plays, 32,000 likes
Top mentions:
@devtools_review | 120,000 plays | Best SERP APIs ranked for developers
@startup_hacks | 89,000 plays | I replaced my web scraper with this
Sentiment Analysis (45 comments):
positive: 28 (62%)
"This is exactly what I needed for my project"
negative: 5 (11%)
neutral: 12 (27%)
Digest cost: $0.030