> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Competitor Sentiment Tracking
> Search Reddit for competitor mentions across subreddits, enrich with comments, and produce structured sentiment breakdowns via Mave
## Scenario
Competitors are discussed daily on Reddit. This job searches for mentions across subreddits, feeds text into Mavera Chat, and produces a structured sentiment breakdown: positive, negative, neutral, and opportunity. Run weekly.
## Architecture
```mermaid theme={"dark"}
flowchart LR
A["Reddit GET /r/{sub}/search?q={competitor}"] --> B["Aggregate mentions"]
B --> C["Mavera POST /mave/chat"]
C --> D["Sentiment map"]
```
## Code
```python Python theme={"dark"}
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])
```
```javascript JavaScript theme={"dark"}
// --- 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));
```
## Example Output
```text theme={"dark"}
Collected 87 mentions across 3 competitors
## Competitor A — Mixed-Negative
- Positive (34%): Onboarding, integrations, support
- Negative (41%): Pricing ("doubled overnight"), performance
- Opportunity: Pricing transparency + dashboard speed
## Competitor B — Positive
- Positive (62%): UX, mobile, free tier
- Negative (18%): "Outgrows you — no enterprise features"
- Opportunity: Enterprise gap we can fill
```
## Error Handling
Use quoted search or add product-category terms for precision.
N × M API calls. 700ms delays keep you under 100 req/min.
***