Competitor Sentiment Tracking

import os, requests, time

# --- Auth setup same as Job 1 ---

COMPETITORS = ["Competitor A", "Competitor B", "Competitor C"]
SUBS = ["SaaS", "startups", "productivity", "marketing"]

# 1. Search each competitor across subreddits
all_results = []
for comp in COMPETITORS:
    for sub in SUBS:
        r = requests.get(f"{RD}/r/{sub}/search", headers=RD_H,
            params={"q": comp, "restrict_sr": "true", "sort": "relevance", "t": "month", "limit": 10, "raw_json": 1})
        if r.status_code == 429: time.sleep(int(r.headers.get("X-Ratelimit-Reset", 60))); continue
        if not r.ok: continue
        for p in r.json().get("data",{}).get("children",[]):
            d = p["data"]
            all_results.append({"competitor": comp, "subreddit": sub, "title": d.get("title",""),
                "text": d.get("selftext","")[:400], "score": d.get("score",0), "post_id": d.get("id","")})
        time.sleep(0.7)

# 2. Enrich top posts with comments
enriched = []
for item in sorted(all_results, key=lambda x: -x["score"])[:20]:
    enriched.append(f"[/r/{item['subreddit']}] {item['competitor']} | score:{item['score']}\n{item['title']}\n{item['text'][:250]}")
    cr = requests.get(f"{RD}/comments/{item['post_id']}", headers=RD_H,
        params={"limit": 5, "depth": 1, "sort": "top", "raw_json": 1})
    if cr.ok and len(cr.json()) > 1:
        for c in cr.json()[1]["data"]["children"]:
            body = c.get("data",{}).get("body","")
            if body and body not in ("[removed]","[deleted]"):
                enriched.append(f"  COMMENT ({item['competitor']}): {body[:250]}")
    time.sleep(0.7)

# 3. Structured analysis
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Competitive intelligence analyst: analyze Reddit mentions.\n\nCOMPETITORS: {', '.join(COMPETITORS)}\n\nDATA:\n"
        + "\n".join(enriched[:30])
        + "\n\nFor EACH competitor: Sentiment breakdown (positive/negative/neutral with quotes), Top themes, Opportunities for us, Sentiment trend. End with comparative ranking."
}).json()

print(f"Collected {len(all_results)} mentions across {len(COMPETITORS)} competitors")
print(analysis.get("content", "")[:2000])

// --- Auth setup same as Job 1 ---

const COMPETITORS = ["Competitor A", "Competitor B", "Competitor C"];
const SUBS = ["SaaS", "startups", "productivity", "marketing"];

// 1. Search
const allResults = [];
for (const comp of COMPETITORS) {
  for (const sub of SUBS) {
    const r = await fetch(
      `${RD}/r/${sub}/search?q=${encodeURIComponent(comp)}&restrict_sr=true&sort=relevance&t=month&limit=10&raw_json=1`,
      { headers: RD_H });
    if (r.status === 429) { await new Promise(res => setTimeout(res, 60000)); continue; }
    if (!r.ok) continue;
    for (const p of (await r.json()).data?.children || []) {
      const d = p.data;
      allResults.push({ competitor: comp, subreddit: sub, title: d.title||"",
        text: (d.selftext||"").slice(0,400), score: d.score||0, post_id: d.id||"" });
    }
    await new Promise(r => setTimeout(r, 700));
  }
}

// 2. Enrich + analyze
const enriched = [];
for (const item of allResults.sort((a,b) => b.score - a.score).slice(0, 20)) {
  enriched.push(`[/r/${item.subreddit}] ${item.competitor} | score:${item.score}\n${item.title}\n${item.text.slice(0,250)}`);
  const cr = await fetch(`${RD}/comments/${item.post_id}?limit=5&depth=1&sort=top&raw_json=1`, { headers: RD_H });
  if (cr.ok) for (const c of ((await cr.json())[1]?.data?.children || [])) {
    const body = c.data?.body || "";
    if (body && body !== "[removed]" && body !== "[deleted]")
      enriched.push(`  COMMENT (${item.competitor}): ${body.slice(0,250)}`);
  }
  await new Promise(r => setTimeout(r, 700));
}

const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `Analyze Reddit competitor mentions.\n\nCOMPETITORS: ${COMPETITORS.join(", ")}\n\nDATA:\n${enriched.slice(0,30).join("\n")}\n\nFor each: sentiment breakdown, themes, opportunities, trend. Comparative ranking.` }),
}).then(r => r.json());

console.log(`Collected ${allResults.length} mentions`);
console.log((analysis.content || "").slice(0, 2000));