Cómo construir una escucha social multiplataforma

import os, requests, json
from datetime import datetime
from collections import Counter, defaultdict

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}

BRAND = 'Scavio'
COMPETITORS = ['Tavily', 'SerpAPI']

def collect_mentions(brand):
    mentions = []
    # Google web mentions
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': brand, 'country_code': 'us'}).json()
    for r in data.get('organic_results', [])[:5]:
        mentions.append({'platform': 'google', 'title': r.get('title', ''), 'text': r.get('snippet', ''), 'link': r.get('link', ''), 'reach': 0})
    # Reddit mentions
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': brand, 'platform': 'reddit', 'country_code': 'us'}).json()
    for r in data.get('organic_results', [])[:5]:
        mentions.append({'platform': 'reddit', 'title': r.get('title', ''), 'text': r.get('snippet', ''), 'link': r.get('link', ''), 'reach': 0})
    # YouTube mentions
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': brand, 'platform': 'youtube', 'country_code': 'us'}).json()
    for r in data.get('organic_results', [])[:5]:
        mentions.append({'platform': 'youtube', 'title': r.get('title', ''), 'text': r.get('snippet', ''), 'link': r.get('link', ''), 'reach': 0})
    # TikTok mentions
    data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
        headers=TH, json={'query': brand}).json()
    for v in (data.get('videos', data.get('data', {}).get('videos', [])))[:5]:
        mentions.append({'platform': 'tiktok', 'title': v.get('desc', '')[:80], 'text': v.get('desc', ''), 'link': '', 'reach': v.get('stats', {}).get('playCount', 0)})
    return mentions

all_mentions = collect_mentions(BRAND)
by_platform = Counter(m['platform'] for m in all_mentions)
print(f'{BRAND}: {len(all_mentions)} total mentions')
for p, c in by_platform.most_common():
    print(f'  {p}: {c} mentions')
print(f'Cost: $0.020')

POSITIVE = ['best', 'great', 'love', 'recommend', 'amazing', 'excellent', 'game changer', 'solid']
NEGATIVE = ['worst', 'terrible', 'avoid', 'hate', 'broken', 'expensive', 'scam', 'disappointed']

def score_sentiment(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_by_platform(mentions):
    results = defaultdict(lambda: Counter())
    for m in mentions:
        sentiment = score_sentiment(f'{m["title"]} {m["text"]}')
        m['sentiment'] = sentiment
        results[m['platform']][sentiment] += 1
    print(f'\n=== Sentiment by Platform ===')
    for platform, sentiments in results.items():
        total = sum(sentiments.values())
        pos_pct = sentiments['positive'] / total * 100 if total else 0
        neg_pct = sentiments['negative'] / total * 100 if total else 0
        print(f'  {platform:10} | +{sentiments["positive"]} ~{sentiments["neutral"]} -{sentiments["negative"]} | {pos_pct:.0f}% positive')
    return results

sentiment_by_platform(all_mentions)

def social_listening_report(brand, mentions, competitors):
    print(f'\n{"=" * 60}')
    print(f'  SOCIAL LISTENING REPORT - {brand}')
    print(f'  Date: {datetime.now().strftime("%Y-%m-%d")}')
    print(f'{"=" * 60}')
    # Overview
    total = len(mentions)
    positive = sum(1 for m in mentions if m.get('sentiment') == 'positive')
    negative = sum(1 for m in mentions if m.get('sentiment') == 'negative')
    total_reach = sum(m.get('reach', 0) for m in mentions)
    print(f'\n  Mentions: {total} | Reach: {total_reach:,}')
    print(f'  Sentiment: +{positive} ~{total-positive-negative} -{negative}')
    # Platform breakdown
    print(f'\n  Platform Breakdown:')
    by_platform = defaultdict(list)
    for m in mentions:
        by_platform[m['platform']].append(m)
    for p, items in by_platform.items():
        reach = sum(m.get('reach', 0) for m in items)
        print(f'    {p:10} | {len(items):3} mentions | reach: {reach:,}')
    # Top mentions by reach
    high_reach = sorted(mentions, key=lambda m: m.get('reach', 0), reverse=True)[:3]
    if any(m.get('reach', 0) > 0 for m in high_reach):
        print(f'\n  Top by Reach:')
        for m in high_reach:
            if m.get('reach', 0) > 0:
                print(f'    [{m["platform"]}] {m["reach"]:,} reach: {m["title"][:40]}')
    # Competitor comparison
    if competitors:
        print(f'\n  Competitor Comparison:')
        for comp in competitors:
            comp_mentions = collect_mentions(comp)
            print(f'    {comp:15} | {len(comp_mentions)} mentions')
    print(f'\n  Cost: $0.020 per brand scan')

social_listening_report(BRAND, all_mentions, COMPETITORS)