Create mentions_dashboard.py

mentions_dashboard.py

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+# mentions_dashboard.py
+
+import json
+import time
+import re
+import os
+from datetime import datetime
+
+from urllib.parse import quote
+from requests_oauthlib import OAuth2Session
+from textblob import TextBlob
+import matplotlib.pyplot as plt
+
+
+def extract_text_from_commentary(commentary):
+    return re.sub(r"{.*?}", "", commentary).strip()
+
+
+def analyze_sentiment(text):
+    return TextBlob(text).sentiment.polarity
+
+
+def generate_mentions_dashboard(comm_client_id, comm_token_dict):
+    linkedin = OAuth2Session(comm_client_id, token=comm_token_dict)
+
+    org_urn = "urn:li:organization:19010008"
+    encoded_urn = quote(org_urn, safe='')
+
+    linkedin.headers.update({
+        "LinkedIn-Version": "202502",
+        "X-Restli-Protocol-Version": "2.0.0"
+    })
+
+    base_url = (
+        "https://api.linkedin.com/rest/organizationalEntityNotifications"
+        "?q=criteria"
+        "&actions=List(COMMENT,SHARE_MENTION)"
+        f"&organizationalEntity={encoded_urn}"
+        "&count=50"
+    )
+
+    all_notifications = []
+    start = 0
+
+    while True:
+        url = f"{base_url}&start={start}"
+        resp = linkedin.get(url)
+        if resp.status_code != 200:
+            break
+
+        data = resp.json()
+        elements = data.get("elements", [])
+        all_notifications.extend(elements)
+
+        if len(elements) < data.get("paging", {}).get("count", 0):
+            break
+
+        start += len(elements)
+        time.sleep(0.5)
+
+    # Extract mentions and their share URNs
+    mention_shares = [e.get("generatedActivity") for e in all_notifications if e.get("action") == "SHARE_MENTION"]
+    mention_data = []
+
+    for share_urn in mention_shares:
+        if not share_urn:
+            continue
+        encoded_share_urn = quote(share_urn, safe='')
+        share_url = f"https://api.linkedin.com/rest/posts/{encoded_share_urn}"
+        response = linkedin.get(share_url)
+
+        if response.status_code != 200:
+            continue
+
+        post = response.json()
+        commentary_raw = post.get("commentary", "")
+        if not commentary_raw:
+            continue
+
+        commentary = extract_text_from_commentary(commentary_raw)
+        sentiment = analyze_sentiment(commentary)
+        timestamp = post.get("createdAt", 0)
+        dt = datetime.fromtimestamp(timestamp / 1000.0)
+
+        mention_data.append({
+            "date": dt,
+            "text": commentary,
+            "sentiment": sentiment
+        })
+
+    # Save HTML
+    html_parts = ["<h2 style='text-align:center;'>📣 Mentions Sentiment Dashboard</h2>"]
+    for mention in mention_data:
+        html_parts.append(f"""
+            <div style='border:1px solid #ccc; border-radius:10px; padding:15px; margin:10px;'>
+                <p><strong>Date:</strong> {mention["date"].strftime('%Y-%m-%d')}</p>
+                <p>{mention["text"]}</p>
+                <p><strong>Sentiment:</strong> {mention["sentiment"]:.2f}</p>
+            </div>
+        """)
+
+    html_path = "mentions_dashboard.html"
+    with open(html_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(html_parts))
+
+    # Plot
+    if mention_data:
+        dates = [m["date"] for m in mention_data]
+        sentiments = [m["sentiment"] for m in mention_data]
+
+        plt.figure(figsize=(10, 5))
+        plt.plot(dates, sentiments, marker='o', linestyle='-', color='blue')
+        plt.title("Sentiment Over Time")
+        plt.xlabel("Date")
+        plt.ylabel("Sentiment")
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig("mentions_sentiment_plot.png")
+        plt.close()
+
+    return html_path