Sentiment analysis across a Reddit thread tells you at a glance whether a community is enthusiastic, skeptical, or split. With a single API call you can pull the post plus all comments, and with a short LLM prompt you can classify each reply and aggregate. This tutorial uses Scavio for the fetch and Anthropic's Claude for the classification, but the structure works for any provider.
Prerequisites
- Python 3.10+
- anthropic and requests libraries
- A Scavio API key and an Anthropic API key
- A Reddit thread URL to analyze
Walkthrough
Step 1: Fetch the thread
Pull the post plus all comments in one call.
Python
import os, requests
resp = requests.post(
"https://api.scavio.dev/api/v1/reddit/post",
headers={"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"},
json={"url": "https://www.reddit.com/r/Python/comments/1smb9du/"},
timeout=30,
)
comments = resp.json()["data"]["comments"]Step 2: Classify each comment
Send each comment body to Claude and ask for a single label: positive, neutral, or negative.
Python
from anthropic import Anthropic
client = Anthropic()
def classify(text: str) -> str:
msg = client.messages.create(
model="claude-opus-4-6",
max_tokens=10,
messages=[{"role": "user", "content": f"One word: positive, neutral, or negative?\n\n{text}"}],
)
return msg.content[0].text.strip().lower()Step 3: Aggregate and report
Run classify over top-level comments, count labels, and print a summary.
Python
from collections import Counter
top_level = [c for c in comments if c["depth"] == 0][:25]
labels = [classify(c["body"]) for c in top_level]
print(Counter(labels))Python Example
Python
import os, requests
from collections import Counter
from anthropic import Anthropic
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
client = Anthropic()
def fetch_thread(url: str):
r = requests.post(
"https://api.scavio.dev/api/v1/reddit/post",
headers={"Authorization": f"Bearer {SCAVIO_KEY}"},
json={"url": url},
timeout=30,
)
r.raise_for_status()
return r.json()["data"]
def classify(text: str) -> str:
msg = client.messages.create(
model="claude-opus-4-6",
max_tokens=10,
messages=[{"role": "user", "content": f"One word: positive, neutral, or negative?\n\n{text}"}],
)
return msg.content[0].text.strip().lower()
data = fetch_thread("https://www.reddit.com/r/Python/comments/1smb9du/")
top = [c for c in data["comments"] if c["depth"] == 0][:25]
labels = [classify(c["body"]) for c in top]
print("Sentiment on", data["post"]["title"])
print(Counter(labels))JavaScript Example
JavaScript
import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function fetchThread(url) {
const r = await fetch("https://api.scavio.dev/api/v1/reddit/post", {
method: "POST",
headers: {
Authorization: `Bearer ${SCAVIO_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({ url }),
});
return (await r.json()).data;
}
async function classify(text) {
const msg = await client.messages.create({
model: "claude-opus-4-6",
max_tokens: 10,
messages: [{ role: "user", content: `One word: positive, neutral, or negative?\n\n${text}` }],
});
return msg.content[0].text.trim().toLowerCase();
}
const data = await fetchThread("https://www.reddit.com/r/Python/comments/1smb9du/");
const top = data.comments.filter((c) => c.depth === 0).slice(0, 25);
const labels = await Promise.all(top.map((c) => classify(c.body)));
const counts = labels.reduce((a, l) => ({ ...a, [l]: (a[l] ?? 0) + 1 }), {});
console.log(counts);Expected Output
JSON
Sentiment on FastAPI vs Django in 2026 -- what the teams are actually using
Counter({'positive': 14, 'neutral': 8, 'negative': 3})