Analyse crypto podcasts programmatically

import json, urllib.request, statistics
from urllib.parse import urlencode
BASE = "https://api.alphaday.com"

def get(path, **p):
    with urllib.request.urlopen(f"{BASE}{path}?{urlencode(p)}") as r:
        return json.load(r)

TAG = "ethereum"
pods = get("/items/podcasts/", tags=TAG, period=1, limit=8)   # 1 = LAST_WEEK
news = get("/items/news/", tags=TAG, period=1, limit=50)["results"]

# The news half needs no model — the scores are already on the rows.
scores = [float(n["sentiment_score"]) for n in news if n.get("sentiment_score") is not None]
print(f"news sentiment over {len(scores)} articles this week: "
      f"mean {statistics.mean(scores):+.2f}, median {statistics.median(scores):+.2f}")
print(f"podcast episodes mentioning {TAG} this week: {pods['total']}\n")

print("Episodes to feed an LLM (title + description, no transcript needed):")
for p in pods["results"][:5]:
    desc = (p.get("short_description") or "").strip().replace("\n", " ")
    print(f"- {p['source']['name']}: {p['title'][:58]}")
    print(f"    {desc[:96]}...")

Classify each episode's stance on Ethereum as bullish, bearish or neutral.
Return one JSON object per episode with the title and a one-line reason.