News Sentiment Tracking

import os, requests, time
from datetime import datetime, timedelta

NA_KEY = os.environ["NEWSAPI_KEY"]
NA_BASE = "https://newsapi.org/v2"
NA_H = {"X-Api-Key": NA_KEY}
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

TOPIC = "artificial intelligence marketing"
WEEKS = 4

# 1. Fetch articles in weekly batches
weekly_data = []
for week in range(WEEKS):
    end = datetime.now() - timedelta(weeks=week)
    start = end - timedelta(days=7)
    r = requests.get(f"{NA_BASE}/everything", headers=NA_H, params={
        "q": TOPIC, "from": start.strftime("%Y-%m-%d"),
        "to": end.strftime("%Y-%m-%d"), "sortBy": "relevancy",
        "language": "en", "pageSize": 25,
    })
    if not r.ok:
        print(f"Week {week}: API error {r.status_code}")
        continue
    articles = r.json().get("articles", [])
    weekly_data.append({
        "week_label": f"{start.strftime('%b %d')} – {end.strftime('%b %d')}",
        "articles": articles, "count": len(articles),
    })
    print(f"Week {week}: {len(articles)} articles ({start.strftime('%b %d')} – {end.strftime('%b %d')})")
    time.sleep(1)

# 2. Batch sentiment analysis via Mave
sentiment_series = []
for week in weekly_data:
    corpus = "\n".join(
        f"- [{a.get('source',{}).get('name','')}] {a['title']}: {a.get('description','')[:150]}"
        for a in week["articles"][:20]
    )
    analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
        "message": f"Sentiment analyst. Analyze media coverage for '{TOPIC}' during {week['week_label']}.\n\n"
            f"ARTICLES ({week['count']}):\n{corpus}\n\n"
            "Return JSON with: overall_sentiment (positive/negative/neutral/mixed), "
            "confidence (0-100), positive_pct, negative_pct, neutral_pct, "
            "top_positive_theme, top_negative_theme, notable_shift, key_quote."
    }).json()
    content = analysis.get("content", "")
    sentiment_series.append({"week": week["week_label"], "count": week["count"], "analysis": content[:600]})
    time.sleep(0.5)

# 3. Trend summary
trend = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Given these {WEEKS} weeks of sentiment data for '{TOPIC}', identify the overall trend.\n\n"
        + "\n\n".join(f"**{s['week']}** ({s['count']} articles):\n{s['analysis']}" for s in sentiment_series)
        + "\n\nProvide: trend direction (improving/declining/stable), confidence, biggest driver, prediction for next week, recommended action."
}).json()

print(f"\n{'='*60}\nSENTIMENT TREND: {TOPIC}\n{'='*60}")
for s in sentiment_series:
    print(f"\n{s['week']} ({s['count']} articles):\n{s['analysis'][:300]}")
print(f"\n{'='*60}\nTREND ANALYSIS\n{'='*60}")
print(trend.get("content", "")[:800])

const NA_KEY = process.env.NEWSAPI_KEY;
const NA_BASE = "https://newsapi.org/v2";
const NA_H = { "X-Api-Key": NA_KEY };
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const TOPIC = "artificial intelligence marketing";
const WEEKS = 4;

// 1. Weekly batches
const weeklyData = [];
for (let week = 0; week < WEEKS; week++) {
  const end = new Date(Date.now() - week * 7 * 86400000);
  const start = new Date(end - 7 * 86400000);
  const r = await fetch(
    `${NA_BASE}/everything?q=${encodeURIComponent(TOPIC)}&from=${start.toISOString().slice(0,10)}&to=${end.toISOString().slice(0,10)}&sortBy=relevancy&language=en&pageSize=25`,
    { headers: NA_H });
  if (!r.ok) { console.log(`Week ${week}: error ${r.status}`); continue; }
  const articles = (await r.json()).articles || [];
  const label = `${start.toLocaleDateString("en",{month:"short",day:"numeric"})} – ${end.toLocaleDateString("en",{month:"short",day:"numeric"})}`;
  weeklyData.push({ week_label: label, articles, count: articles.length });
  console.log(`Week ${week}: ${articles.length} articles (${label})`);
  await new Promise(r => setTimeout(r, 1000));
}

// 2. Batch sentiment
const sentimentSeries = [];
for (const week of weeklyData) {
  const corpus = week.articles.slice(0, 20)
    .map(a => `- [${a.source?.name||""}] ${a.title}: ${(a.description||"").slice(0,150)}`).join("\n");
  const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ message: `Sentiment analyst. '${TOPIC}' coverage for ${week.week_label}.\n\nARTICLES (${week.count}):\n${corpus}\n\nReturn JSON: overall_sentiment, confidence, positive_pct, negative_pct, neutral_pct, top themes, key_quote.` }),
  }).then(r => r.json());
  sentimentSeries.push({ week: week.week_label, count: week.count, analysis: (analysis.content||"").slice(0,600) });
  await new Promise(r => setTimeout(r, 500));
}

// 3. Trend
const trend = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `${WEEKS}-week sentiment trend for '${TOPIC}'.\n\n${sentimentSeries.map(s=>`**${s.week}** (${s.count} articles):\n${s.analysis}`).join("\n\n")}\n\nTrend direction, confidence, biggest driver, next-week prediction, recommended action.` }),
}).then(r => r.json());

console.log(`\n${"=".repeat(60)}\nSENTIMENT TREND: ${TOPIC}`);
for (const s of sentimentSeries)
  console.log(`\n${s.week} (${s.count} articles):\n${s.analysis.slice(0,300)}`);
console.log(`\n${"=".repeat(60)}\nTREND ANALYSIS`);
console.log((trend.content || "").slice(0, 800));