Scrape YouTube Comments: API, Python, No-code & GoProxy (Beginner → Pro)

Scraping YouTube comments is one of the fastest ways to analyze audience sentiment, build datasets for machine learning, and track engagement trends. But how do you do it effectively without running afoul of rules or getting your IP blocked? This comprehensive guide covers everything from beginner-friendly no-code methods to advanced Python scripts, with a focus on practical steps and common pitfalls.

Who this is for:

1. Start with the YouTube Data API for simple, low-risk, structured, repeatable, and few pulls.

2. Use Selenium/Playwright only for UI-only data (pinned comments, some Shorts, live chat). Use no-code scrapers for fast, non-technical exports. Use GoProxy responsibly for geo-targeting and distributing UI requests—do not use proxies to bypass API quotas or platform policies.

Why Scrape YouTube Comments?

YouTube boasts over 2 billion monthly users, generating millions of comments daily. Scraping them is one of the fastest ways to analyze audience sentiment, build datasets for machine learning, and track engagement trends. 

Common uses include:

Sentiment analysis around product releases or events (e.g., gauging reactions to a new tech gadget).

Market research: Discover complaints, feature requests, or emerging trends in viewer feedback.

Content optimization: Mine competitor comments to identify topic gaps or high-engagement ideas.

Training datasets for AI/ML models (label carefully; respect privacy and avoid biases).

Legal, Ethical & Operational Notes (Read First)

Prefer the API where possible—it’s the supported, lower-risk route.

Do NOT use proxies to evade API quotas or bypass platform enforcement; that’s both unethical and risky; they're for geo-targeting, distributing legitimate requests, and avoiding local throttles.

Don’t collect sensitive personal data. If you publish results, anonymize authors or aggregate.

Commercial use: get legal review to ensure compliance with YouTube’s Terms and local law.

Quick Method Decision

This short guide helps you choose first, then dive into the detailed implementations below.

Method	Use when	Pros	Cons	Difficulty	Difficulty Setup Time Estimate
YouTube Data API	Structured, repeatable pulls of public/top-level comments; large-scale	Official, stable, low ban risk	Requires API key, quotas, extra calls for replies	Medium	10-20 mins
Browser Automation (Selenium/Playwright)	UI-only fields (e.g., pinned comments, Shorts, live chat) or logged-in simulation	Captures exact UI; handles lazy-loading	Fragile to UI changes, heavier resource use; requires proxy/fingerprint strategy	Medium-High	15-30 mins
No-code / SaaS Scrapers	Quick exports or non-technical teams need scheduled jobs	Fast, visual, low setup	Less customizable, potential costs	Easy	5-10 mins

Validate with the API first (one video). If missing required fields, add a short Selenium test to confirm UI needs, then decide hybrid pipeline.

Method 1. YouTube Data API

Why start here?

It's structured, reliable, and official—ideal for most pipelines.

What the API returns (common fields)

authorDisplayName, publishedAt, textDisplay/textOriginal, likeCount, replyCount, isPublic. Replies require extra calls.

Prerequisites

Create Google Cloud project → enable YouTube Data API v3.

Create an API key (or OAuth if accessing private resources).

Monitor quota in Cloud Console (commentThreads and comments endpoints use quota units).

1. Install libraries

Run in terminal:

pip install google-api-python-client pandas

2. Copy-and-run starter script (fetches top-level comments)

# api_comments.py

import re, time, pandas as pd

from googleapiclient.discovery import build

 

API_KEY = "YOUR_API_KEY"

youtube = build("youtube", "v3", developerKey=API_KEY)

 

def extract_video_id(url):

    m = re.search(r'(?:v=|youtu\.be/|/v/)([A-Za-z0-9_-]{11})', url)

    return m.group(1) if m else None

 

def get_comments_api(video_id):

    comments = []

    request = youtube.commentThreads().list(

        part="snippet",

        videoId=video_id,

        maxResults=100,

        textFormat="plainText"

    )

    while request:

        response = request.execute()

        for item in response.get('items', []):

            s = item['snippet']['topLevelComment']['snippet']

            comments.append({

                'comment_id': item['id'],

                'author': s.get('authorDisplayName'),

                'text': s.get('textDisplay'),

                'likes': int(s.get('likeCount') or 0),

                'publishedAt': s.get('publishedAt'),

                'replyCount': int(s.get('replyCount') or 0)

            })

        request = youtube.commentThreads().list_next(request, response)

        time.sleep(0.1)

    return comments

 

if __name__ == "__main__":

    url = "https://www.youtube.com/watch?v=VIDEO_ID"

    vid = extract_video_id(url)

    df = pd.DataFrame(get_comments_api(vid))

    df.to_csv("youtube_comments_api.csv", index=False)

    print("Saved", len(df), "comments")

3. Fetch replies

Extend for threaded data; costs extra quota:

def get_replies(parent_id):

    replies = []

    req = youtube.comments().list(part="snippet", parentId=parent_id, maxResults=100)

    while req:

        resp = req.execute()

        for it in resp.get('items', []):

            s = it['snippet']

            replies.append({

                'reply_id': it['id'],

                'parent_id': parent_id,

                'author': s.get('authorDisplayName'),

                'text': s.get('textDisplay'),

                'likes': int(s.get('likeCount') or 0),

                'publishedAt': s.get('publishedAt')

            })

        req = youtube.comments().list_next(req, resp)

    return replies

4. Backoff Helper

For robust error handling:

import random, time

def with_backoff(func, max_retries=5, base=0.5):

    for attempt in range(1, max_retries+1):

        try:

            return func()

        except Exception as e:

            if attempt == max_retries:

                raise

            sleep = base * (2 ** (attempt - 1)) + random.uniform(0, base)

            time.sleep(sleep)

• Wrap heavy API calls in with_backoff and log failures.

Best practices

Monitor quota in Google Cloud Console.

Use pagination with nextPageToken fully.

Don’t use proxies to bypass API quota enforcement.

For pros: Batch multiple videos in a loop.

Common pitfall for beginners

403 Unauthorized — check API key, enable API, ensure billing if needed.

429 Too Many Requests — slow down, implement backoff, check quotas. Don’t try to bypass quotas with proxying.

Method 2. Browser Automation with Selenium

When you’ll actually use it

Only when the API cannot provide the data you need (pinned comments, some Shorts, embedded UIs, or simulation of logged-in users).

Prerequisites

Python 3.x, pip install selenium pandas selenium-wire (selenium-wire if using proxy auth).

Download ChromeDriver matching your Chrome versionSet Up Environment

1. Import Libraries

from selenium import webdriver

from selenium.webdriver.common.by import By

from selenium.webdriver.common.keys import Keys

from time import sleep

import pandas as pd

2. Initialize Browser

driver = webdriver.Chrome()  # Add executable_path if needed

driver.maximize_window()

3. Navigate and Scroll

Open the video URL, scroll to load comments:

url = "https://www.youtube.com/watch?v=VIDEO_ID"

driver.get(url)

sleep(5)  # Wait for load

for _ in range(200):  # Adjust for more comments

    driver.execute_script("window.scrollBy(0, 700)")

    sleep(2)  # Pause to load

4. Extract Comments

Use XPath; test in browser DevTools: Right-click element > Inspect > Copy XPath:

comments = []

comment_elements = driver.find_elements(By.XPATH, '//*[@id="content-text"]')

for elem in comment_elements:

    comments.append(elem.text)

5. Export Data

df = pd.DataFrame(comments, columns=['Comment'])

df.to_csv('youtube_comments.csv', index=False)

driver.quit()

6. Incorporate Proxies

from seleniumwire import webdriver

 

proxy_opts = {

  'proxy': {

    'http': 'http://username:password@proxy-host:port',

    'https': 'http://username:password@proxy-host:port'

  }

}

options = webdriver.ChromeOptions()

options.add_argument('--start-maximized')

driver = webdriver.Chrome(seleniumwire_options=proxy_opts, options=options)

If your proxy uses IP allowlisting, use options.add_argument('--proxy-server=http://host:port').

Best practices

Use WebDriverWait for expected elements instead of blind sleeps.

Randomize intervals between scrolls and actions.

Persist browser profiles (cookies) for logged-in scrapes (sticky sessions).

Use headful mode for reliability; headless is detectable and brittle for long runs.

Common pitfall for beginners

If Selenium returns zero comments: validate DOM selectors in DevTools (right-click → Inspect → Copy → selector/XPath), increase wait times.

Method 3. No-code / SaaS Scrapers

1. Choose a reputable scraper supporting JS rendering and proxies, popular options like Apify.

2. Paste video URLs or upload a CSV of video IDs.

3. Configure fields (comment text, author, date, likes, replies).

4. Test on a short video (100 comments). Verify output (e.g., encoding for emojis).

5. If bulk scraping, configure a proxy pool (GoProxy) on the tool if supported and rotate every 10–50 requests depending on risk.

Note: No-code tools are perfect; please verify field coverage (replies/pinned comments) before scaling.

Proxy Integration for Overcoming Challenges

GoProxy excels for geo-targeting, session stickiness, and distribution—never for bypassing API quotas or platform rules. Use mobile proxies or residential proxies (mimic real users) over datacenter for YouTube. 

Auth mode: username:password or IP allowlist (use exactly what GoProxy provides).

Rotation strategy: rotate every N requests or on errors (suggested: 5–50).

Concurrency per IP: start at 1–5 parallel jobs per residential IP; increase slowly.

Session stickiness: use sticky session(can up to 60min) for logged-in sessions when needed.

Geo-targeting: select country-specific endpoints for regional views.

Monitoring: log proxy id, latency, status codes, errors; alert on >5% error rate.

Implementation note: If using googleapiclient, proxy support is more complex—set HTTP(S)_PROXY env vars or use requests wrapper for explicit proxy control for non-API UI calls.

Data Cleaning, Storage & Basic Analysis

Suggested Schema

comment_id, video_id, parent_id, author, author_channel_id, text, likes, reply_count, published_at, scraped_at, source_method.

Cleaning Steps

Convert publishedAt to ISO UTC: df['publishedAt'] = pd.to_datetime(df['publishedAt']).

Numerics to int: df['likes'] = df['likes'].astype(int).

Deduplicate: df.drop_duplicates(subset=['comment_id']).

Handle emojis: Preserve or normalize as needed.

Sentiment Analysis (Quick Start)

from textblob import TextBlob

df['polarity'] = df['text'].apply(lambda t: TextBlob(t).sentiment.polarity)

Pro Tip: Upgrade to transformer models (e.g., Hugging Face) for accuracy. 

Storage

Small: CSV.

Medium: Parquet + S3 (partition by video_id/date).

Large: Columnar DB (BigQuery, ClickHouse).

Scaling, Monitoring & Anti-detection

Hybrid architecture

API workers for bulk top-level comments.

Browser workers for UI-only content.

Orchestrator (queue) to dispatch video IDs to workers; proxy manager to assign IPs.

Observability

Track: requests/sec, success rate, avg latency, proxy failure rate.

Log: video_id, proxy_id, status_code, parse_errors.

Alerts: >5% error rate; CAPTCHAs spike; repeated 429/403.

Anti-detection

Randomize timing; rotate user-agents; use real browser profiles; moderate concurrency.

Use residential proxies for human-like IP behavior.

Avoid headless mode for long runs.

Troubleshooting Common Issues

403 (API) — Check key, enable API, activate billing.

429 — Backoff and reduce request rate; check quota. Do not bypass with proxies.

Empty Selenium results — Update selectors in DevTools; increase waits.

CAPTCHA frequency — Reduce concurrency; switch to residential proxies; add randomness.

FAQs

Can I scrape private videos? Only with proper OAuth scopes and explicit permission.

Does the API return replies? No by default—use replies endpoint.

Can I scrape live chat? Use liveChatMessages API for authorized streams; UI scraping for other cases (complex).

How many requests/month? Depends on API quota and proxy capacity — start small and monitor.

Final Thoughts

Scraping YouTube comments gives fast, actionable insights for product, marketing, and moderation workflows — but prioritize ethics, quotas, and data privacy. Start small, build observability, and expand with hybrid pipelines when necessary.

If you want reliable IP distribution and geo-targeting for your scraping pipeline, try GoProxy’s geo-targeted rotating residential proxies — sign up for a free trial and test proxy health with a pilot today.