DEV Community: Monkey.D.Scraping

What are people actually saying under that post? Facebook & Instagram comment sentiment with Python

Monkey.D.Scraping — Mon, 09 Nov 2026 21:17:52 +0000

You launched something. The post has 400 comments. Your boss asks "so, how did it go?" and the honest answer is "the vibes are mixed".

We can do better than vibes.

Disclosure up front: I built the comment scrapers and the repo used below. They're pay-per-result tools on Apify, and a test run costs a couple of cents.

The plan

Pull every public comment from a Facebook or Instagram post (no login).
Give each comment a sentiment and a short topic.
Get a CSV plus a one-screen summary per post.

All the code is in social-comments-sentiment.

Step 1: get the comments

The comments come from two Apify Actors, one per platform: Instagram Comments Scraper and Facebook Comments Scraper. From Python it's one call (pip install apify-client):

import os
from apify_client import ApifyClient

apify = ApifyClient(os.environ["APIFY_TOKEN"])
run = apify.actor("monkey.d.scraping/instagram-comments-scraper").call(
    run_input={"startUrls": [{"url": "https://www.instagram.com/p/DDIJAfeyemG/"}], "maxComments": 100},
    max_total_charge_usd=0.05,
    logger=None,
)
comments = list(apify.dataset(run.default_dataset_id).iterate_items())

Each row has the text, author, date, likes and a link to the comment. Facebook works the same way with facebook-comments-scraper, and also gives you replies.

While writing this I ran it with 10 comments: done in 3 seconds, cost $0.0018.

Price check, because it matters at scale: $0.18 per 1,000 comments on the Free plan, less on paid plans. The most used Instagram comment scraper on the Store charges $2.60 per 1,000 on the same plan. Same comments, about 14× less.

Step 2: sentiment that understands emoji (and Spanish)

First try: VADER, the classic free sentiment library. On a real Instagram post it found 9 positive comments out of 29. It read "❤️🙌🔥" as negative and had no idea what to do with Spanish. Fine for English tweets in 2014, not great for comment sections in 2026.

Second try: send the comments to Claude, 50 at a time, and ask for structured JSON:

{"results": [{"i": 0, "sentiment": "positive", "topic": "heart emojis"}]}

Same post: 19 positive, 8 neutral, 1 negative, 1 mixed. It gets emoji, other languages and sarcasm, and the topics are actually useful: "emoji reaction", "love for instagram", "ai generated content". Cost: roughly half a dollar per 1,000 comments with the default model, less with a smaller one.

The repo does both: Claude if you have an Anthropic key, VADER if you don't.

Step 3: the summary

python sentiment.py https://www.instagram.com/p/DDIJAfeyemG/

## https://www.instagram.com/p/DDIJAfeyemG/
29 comments: positive 19 (65%), neutral 8 (27%), negative 1 (3%), mixed 1 (3%)
Top topics: emoji reaction (5), laughing emojis (3), heart emojis (2), applause emojis (2)

You also get the most liked negative comments per post. That's usually where the real feedback is hiding.

What I'd use it for

Comparing how two launches landed, with numbers instead of vibes.
Reading a competitor's comments: what do their customers complain about?
Feeding a weekly report, or an n8n workflow that writes to Google Sheets.

TL;DR

Public comments only, no login, no browser on your side.
A few cents per post with Claude, free with VADER.
Everything is in the repo: social-comments-sentiment.

Questions, or a platform you want next? Comments are open. I read them, obviously.

Written with some help from an AI assistant. Code, prices and the test runs were checked by me.

How I monitor brand mentions on Telegram (no account, no phone number)

Monkey.D.Scraping — Mon, 09 Nov 2026 21:04:04 +0000

A lot of stuff hits Telegram before it hits X or the news: crypto launches, leaks, product complaints, regional news. If you do OSINT, PR or brand monitoring, you want to know when a name shows up in public channels.

Two problems:

Telegram has no public "search all channels" page.
The official API wants a phone number and an account.

So I built a tool for it: Telegram Keyword Search. Disclosure up front: it's mine, it runs on Apify and it's pay-per-result. A first test costs about one cent.

What we want

For a keyword (your brand, a product, a competitor), a table with:

post text
publication date
channel name
a direct link to the original post
views, reactions and media when the channel shows them

…as CSV or JSON, without logging in to Telegram.

Step 1: run a tiny search

Open the five-post starter of Telegram Keyword Search, swap bitcoin for your brand and hit Start. (Or open the Actor page and start from the prefilled input.)

Price, no surprises:

$0.001 per saved post
$0.005 per search page checked
$0.00005 start fee

One search page + five posts = $0.01005. A search that finds nothing costs only the start fee.

Step 2: write a query that isn't dumb

One line = one topic. Inside a line you can combine terms:

nike AND sneakers OR shoes NOT giveaway
"air max" -resale

A post matches only if the terms are literally in the post text. So you don't get random posts from a channel that just happens to have your brand in its name.

Step 3: filter the noise

Under Quality filters you can keep only:

posts with at least N views (minViews)
channels with at least N subscribers (minSubscribers)
original posts, no forwards (excludeForwards)
given languages (postLanguages, e.g. en, ru, zh)
posts with or without media (mediaFilter)

Also set Maximum cost per run in the run options. The run stops there whatever happens. Boring, but it's the setting that lets you sleep.

Step 4: get the data

Each row looks like this:

{
  "channelUsername": "bitcoinnews",
  "messageUrl": "https://t.me/bitcoinnews/16412",
  "publishedAt": "2026-11-09T19:03:02.000Z",
  "views": "36",
  "matchedKeywords": ["bitcoin"],
  "text": "..."
}

Download CSV, Excel or JSON from the run page, or do it from Python (pip install apify-client):

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("monkey.d.scraping/telegram-keyword-search-channel-scraper").call(
    run_input={"keywords": ["your brand"], "maxResults": 20, "afterDate": "7 days"},
    max_total_charge_usd=0.10,
    logger=None,
)
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item["publishedAt"], item["channelUsername"], item["messageUrl"])

I ran the same input with bitcoin and 3 posts while writing this: done in 8 seconds, all three posts from the same day, total cost $0.00805.

Step 5: make it a monitor

Once you know which channels talk about you, watch those instead of all of Telegram. Fresher results, no search fee, and only posts you haven't seen yet.

I put the whole thing in a small open repo, telegram-brand-monitor. It remembers what it already reported, runs every day for free on GitHub Actions and can ping Slack:

python monitor.py "your brand" --channels channel_one,channel_two --days 2

Watching 30 new mentions a day costs about 3 cents. Days with nothing new cost basically nothing.

Prefer no code? Telegram New Posts Monitor does the same on a schedule inside Apify, and n8n, Make or Zapier can forward the results wherever you want.

Already know the channels and want their full history? That's Telegram Channel Scraper.

TL;DR

No Telegram account, no phone number.
Literal matches, with a link to every original post.
Pay per result. First test ≈ one cent.
Public channels only: private groups are out of scope, on purpose.

Got a use case I didn't cover, or something broke? Tell me in the comments, I actually read them.

Written with some help from an AI assistant. Code, prices and the test run were checked by me.