Comment Sentiment → Persona Validation

import os, requests, time

YT = os.environ["YOUTUBE_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
YT_BASE = "https://www.googleapis.com/youtube/v3"
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

VIDEO_ID = "dQw4w9WgXcQ"  # Replace with your video ID

# 1. Fetch up to 200 comments (1 quota unit per page, ~20 per page)
comments = []
page_token = None
while len(comments) < 200:
    params = {
        "key": YT, "videoId": VIDEO_ID,
        "part": "snippet", "maxResults": 100,
        "order": "relevance", "textFormat": "plainText",
    }
    if page_token:
        params["pageToken"] = page_token

    r = requests.get(f"{YT_BASE}/commentThreads", params=params)
    if r.status_code == 403:
        print("Comments disabled or API quota exceeded")
        break
    r.raise_for_status()
    data = r.json()

    for item in data.get("items", []):
        snippet = item["snippet"]["topLevelComment"]["snippet"]
        comments.append({
            "text": snippet["textDisplay"][:500],
            "likes": snippet.get("likeCount", 0),
            "published": snippet.get("publishedAt", ""),
        })

    page_token = data.get("nextPageToken")
    if not page_token:
        break

print(f"Collected {len(comments)} comments")

# 2. Create a research analyst persona
persona = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
    "name": "YouTube Audience Analyst",
    "description": (
        "Senior qualitative researcher specializing in digital audience analysis. "
        "Expert at identifying audience segments from unstructured text, detecting "
        "sentiment patterns, and extracting the language that resonates with each segment. "
        "Thinks in terms of jobs-to-be-done and psychographic clusters, not just demographics."
    ),
}).json()

# 3. Build comment block sorted by engagement
comments.sort(key=lambda c: -c["likes"])
comment_block = "\n".join(
    f"[{c['likes']} likes] {c['text'][:300]}"
    for c in comments[:200]
)

# 4. Analyze via Mave Chat with persona context
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "persona_id": persona["id"],
    "message": f"""Analyze these {len(comments)} YouTube comments as audience segments.

COMMENTS:
{comment_block}

Produce:
1. **Audience Segments** (3-5 clusters): Name each segment, estimate its share of comments, describe their motivation, and quote 2-3 representative comments verbatim.
2. **Sentiment Distribution**: % positive / neutral / negative with examples.
3. **Language Patterns**: Exact phrases, slang, and emotional triggers your marketing should adopt.
4. **Objections & Concerns**: Recurring pain points or skepticism themes.
5. **Content Opportunities**: Topics commenters ask about that you haven't covered.
6. **Persona Validation**: Do these segments match typical B2C buyer personas? Where do they diverge?""",
}).json()

print(f"\nAUDIENCE ANALYSIS — {len(comments)} comments from {VIDEO_ID}")
print("=" * 60)
print(analysis.get("content", "")[:2500])

const YT = process.env.YOUTUBE_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const YT_BASE = "https://www.googleapis.com/youtube/v3";
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const VIDEO_ID = "dQw4w9WgXcQ";

// 1. Fetch up to 200 comments
const comments = [];
let pageToken = null;
while (comments.length < 200) {
  const url = new URL(`${YT_BASE}/commentThreads`);
  url.searchParams.set("key", YT);
  url.searchParams.set("videoId", VIDEO_ID);
  url.searchParams.set("part", "snippet");
  url.searchParams.set("maxResults", "100");
  url.searchParams.set("order", "relevance");
  url.searchParams.set("textFormat", "plainText");
  if (pageToken) url.searchParams.set("pageToken", pageToken);

  const res = await fetch(url);
  if (res.status === 403) {
    console.error("Comments disabled or quota exceeded");
    break;
  }
  const data = await res.json();

  for (const item of data.items || []) {
    const s = item.snippet.topLevelComment.snippet;
    comments.push({
      text: s.textDisplay.slice(0, 500),
      likes: s.likeCount || 0,
      published: s.publishedAt || "",
    });
  }

  pageToken = data.nextPageToken;
  if (!pageToken) break;
}

console.log(`Collected ${comments.length} comments`);

// 2. Research analyst persona
const persona = await fetch(`${MV_BASE}/personas`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "YouTube Audience Analyst",
    description: "Senior qualitative researcher. Expert at segmenting audiences from unstructured text, detecting sentiment, extracting resonant language. Thinks in jobs-to-be-done and psychographic clusters.",
  }),
}).then(r => r.json());

// 3. Build comment block
comments.sort((a, b) => b.likes - a.likes);
const commentBlock = comments.slice(0, 200).map(c =>
  `[${c.likes} likes] ${c.text.slice(0, 300)}`
).join("\n");

// 4. Analyze
const analysis = await fetch(`${MV_BASE}/mave/chat`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    persona_id: persona.id,
    message: `Analyze these ${comments.length} YouTube comments as audience segments.\n\nCOMMENTS:\n${commentBlock}\n\nProduce:\n1. Audience Segments (3-5 clusters) with representative quotes\n2. Sentiment Distribution (% pos/neutral/neg)\n3. Language Patterns to adopt\n4. Objections & Concerns\n5. Content Opportunities\n6. Persona Validation vs typical buyer personas`,
  }),
}).then(r => r.json());

console.log(`\nAUDIENCE ANALYSIS — ${comments.length} comments from ${VIDEO_ID}`);
console.log("=".repeat(60));
console.log((analysis.content || "").slice(0, 2500));