Comment Analysis for Messaging

import os, requests, time

LI = os.environ["LINKEDIN_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
LI_BASE = "https://api.linkedin.com/rest"
MV_BASE = "https://app.mavera.io/api/v1"
LI_H = {"Authorization": f"Bearer {LI}", "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

ORG_URN = "urn:li:organization:12345678"

# 1. Pull recent posts and find high-engagement ones
r = requests.get(f"{LI_BASE}/posts",
    headers=LI_H,
    params={"q": "author", "author": ORG_URN, "count": 50, "sortBy": "LAST_MODIFIED"})
if r.status_code == 429:
    time.sleep(int(r.headers.get("Retry-After", 60)))
    r = requests.get(f"{LI_BASE}/posts", headers=LI_H,
        params={"q": "author", "author": ORG_URN, "count": 50, "sortBy": "LAST_MODIFIED"})
r.raise_for_status()
posts = r.json().get("elements", [])

# 2. Get comment counts to find top posts
post_comments = []
for post in posts:
    post_urn = post.get("id", "")
    commentary = post.get("commentary", "")
    if not commentary:
        continue

    sr = requests.get(f"{LI_BASE}/socialActions/{post_urn}", headers=LI_H)
    if sr.status_code == 429:
        time.sleep(int(sr.headers.get("Retry-After", 30)))
        sr = requests.get(f"{LI_BASE}/socialActions/{post_urn}", headers=LI_H)

    comment_count = 0
    if sr.ok:
        comment_count = sr.json().get("commentsSummary", {}).get("totalFirstLevelComments", 0)

    post_comments.append({
        "urn": post_urn,
        "commentary": commentary[:300],
        "comment_count": comment_count,
    })
    time.sleep(0.3)

# 3. Pull actual comments from top posts (by comment count)
top_posts = sorted(post_comments, key=lambda x: -x["comment_count"])[:8]
all_comments = []

for post in top_posts:
    if post["comment_count"] == 0:
        continue

    cr = requests.get(f"{LI_BASE}/socialActions/{post['urn']}/comments",
        headers=LI_H,
        params={"count": 100})
    if cr.status_code == 429:
        time.sleep(int(cr.headers.get("Retry-After", 30)))
        cr = requests.get(f"{LI_BASE}/socialActions/{post['urn']}/comments",
            headers=LI_H, params={"count": 100})

    if cr.ok:
        comments = cr.json().get("elements", [])
        for c in comments:
            text = c.get("message", {}).get("text", "").strip()
            if text and len(text) > 15:
                all_comments.append({
                    "post_context": post["commentary"][:150],
                    "comment": text[:400],
                })
    time.sleep(0.5)

if not all_comments:
    raise SystemExit("No comments found. Check page permissions and post engagement.")

# 4. Build comment corpus for Mave
corpus = "\n\n---\n\n".join(
    f"[Post context: {c['post_context'][:100]}...]\nComment: {c['comment']}"
    for c in all_comments[:60]
)

# 5. Messaging analysis via Mave
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"""Analyze these {len(all_comments)} LinkedIn comments from our Company Page posts.
These are real prospects and followers reacting to our content.

{corpus}

Produce a messaging intelligence report:

1. QUESTIONS PROSPECTS ASK: What are the top 5-7 recurring questions? Group by theme (pricing, implementation, comparison, capability). Include exact phrasing from comments.

2. OBJECTIONS THAT SURFACE: What resistance or skepticism appears? List the top 5 objections with representative quotes and suggested counter-messaging.

3. LANGUAGE MAP: What words and phrases do commenters use to describe their problems? List the top 10 prospect-native phrases (not our internal jargon). These should be used verbatim in ad copy and landing pages.

4. SENTIMENT CLUSTERS: Group comments by sentiment (enthusiastic, curious, skeptical, critical). What percentage falls in each?

5. CONTENT GAPS: What topics do commenters ask about that we haven't addressed? List 5 content pieces we should create based on comment demand.

6. COMPETITIVE MENTIONS: Any competitors or alternatives mentioned? Context for each."""
}).json()

print(f"Analyzed {len(all_comments)} comments from {len(top_posts)} high-engagement posts")
print("=" * 60)
print(analysis.get("content", "")[:2500])

const LI = process.env.LINKEDIN_ACCESS_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const LI_BASE = "https://api.linkedin.com/rest";
const MV_BASE = "https://app.mavera.io/api/v1";
const LI_H = { Authorization: `Bearer ${LI}`, "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const ORG_URN = "urn:li:organization:12345678";

// 1. Pull recent posts
let res = await fetch(
  `${LI_BASE}/posts?q=author&author=${encodeURIComponent(ORG_URN)}&count=50&sortBy=LAST_MODIFIED`,
  { headers: LI_H }
);
if (res.status === 429) {
  await new Promise(r => setTimeout(r, parseInt(res.headers.get("Retry-After") || "60", 10) * 1000));
  res = await fetch(
    `${LI_BASE}/posts?q=author&author=${encodeURIComponent(ORG_URN)}&count=50&sortBy=LAST_MODIFIED`,
    { headers: LI_H }
  );
}
if (!res.ok) throw new Error(`LinkedIn ${res.status}`);
const posts = (await res.json()).elements || [];

// 2. Find high-engagement posts by comment count
const postComments = [];
for (const post of posts) {
  const commentary = post.commentary || "";
  if (!commentary) continue;
  const sr = await fetch(`${LI_BASE}/socialActions/${post.id}`, { headers: LI_H });
  const commentCount = sr.ok
    ? (await sr.json()).commentsSummary?.totalFirstLevelComments || 0
    : 0;
  postComments.push({ urn: post.id, commentary: commentary.slice(0, 300), commentCount });
  await new Promise(r => setTimeout(r, 300));
}

// 3. Pull comments from top posts
const topPosts = postComments.sort((a, b) => b.commentCount - a.commentCount).slice(0, 8);
const allComments = [];

for (const post of topPosts) {
  if (post.commentCount === 0) continue;
  const cr = await fetch(
    `${LI_BASE}/socialActions/${post.urn}/comments?count=100`,
    { headers: LI_H }
  );
  if (cr.ok) {
    const comments = (await cr.json()).elements || [];
    for (const c of comments) {
      const text = (c.message?.text || "").trim();
      if (text.length > 15) {
        allComments.push({ postContext: post.commentary.slice(0, 150), comment: text.slice(0, 400) });
      }
    }
  }
  await new Promise(r => setTimeout(r, 500));
}

if (!allComments.length) throw new Error("No comments found.");

// 4. Build corpus
const corpus = allComments.slice(0, 60).map(c =>
  `[Post context: ${c.postContext.slice(0, 100)}...]\nComment: ${c.comment}`
).join("\n\n---\n\n");

// 5. Messaging analysis
const analysis = await fetch(`${MV_BASE}/mave/chat`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Analyze these ${allComments.length} LinkedIn comments from our Company Page.\n\n${corpus}\n\nProduce:\n1. QUESTIONS PROSPECTS ASK (top 5-7, grouped by theme, exact phrasing)\n2. OBJECTIONS (top 5 with quotes and counter-messaging)\n3. LANGUAGE MAP (top 10 prospect-native phrases for ad copy)\n4. SENTIMENT CLUSTERS (enthusiastic/curious/skeptical/critical percentages)\n5. CONTENT GAPS (5 pieces to create based on demand)\n6. COMPETITIVE MENTIONS`,
  }),
}).then(r => r.json());

console.log(`Analyzed ${allComments.length} comments from ${topPosts.filter(p => p.commentCount > 0).length} posts`);
console.log("=".repeat(60));
console.log((analysis.content || "").slice(0, 2500));