Cómo desarrollar la escucha social en TikTok

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}')

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}')

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)

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)