Export YouTube Comments With 3 Quick Methods

Want to export all comments from a YouTube video? This guide shows you the three easiest ways to get YouTube comments, with and without coding.

You’ll learn how to quickly download YouTube comment data for sentiment analysis, research, or machine learning, no scraping experience required.

Is It Legal to Scrape YouTube Comments

Generally, yes — scraping publicly available YouTube comments for research or analysis is legal, as long as you respect YouTube’s Terms of Service and avoid collecting personal or private information.

Legal experts and court rulings, such as hiQ Labs v. LinkedIn (2022), have affirmed that accessing public web data does not violate anti-hacking laws when done ethically and responsibly. In other words, extracting publicly visible comments for sentiment analysis or academic study is permissible.

Check out our detail guide that walk thourgh most countrys’ laws about web scraping:

“is web scraping legal“

The thing is, manually copying thousands of comments is just not practical. That’s why most people would use web scrapers to do so. You tell them what you need, and in minutes, all those comments are sitting in a tidy spreadsheet.

Free YouTube Comments Scraper With Code and Without Coding

The previous section was essential to help you understand that YouTube comments scraping is legal. Here we’ll talk about how to get the comments easily online, with coding, and with the best YouTube comment scraper – Octoparse.

Method 1: Export Youtube Comments Online

Octoparse’s YouTube comment scraper allows you to enter up to 10,000 URLs at once, meaning you can export up to ten thousand YouTube video comments simultaneously.

You don’t need to download anything. Just try the YouTube details and comments scraper directly by entering the youtube URL.

Here I tested one viral youtube video with 456 comments on it:

What I love about Octoparse’s online template is that it contains a rich set of data types that I can analyze from the youtube video, such as how many likes one comment has, who left the comment, the date, and etc.

It can also export the comments nicely into spreadsheet:

Well, no more needed to be said here, it’s free and you don’t need to download anything. If you want to see what more it can do, feel free try it yourself:

https://www.octoparse.com/template/youtube-details-comments-scraper

Method 2: Scrape YouTube Comments With Octoparse

Octoparse’s online template is powerful, but they are merely one small part of what Octoparse can do. When you encounter different scenarios like IP blocks and so on, you might need a more comprehensive approach.

Step 1: Paste the YouTube URL to Octoparse

Before beginning, you need to download and install Octoparse on your device, and sign up for a free account.

Copy and paste your desired YouTube video URL in the search bar, and click on the Start button. Octoparse will load the page by auto-detecting.

You can also search YouTube comments on the search bar and find the preset template from method 1.

Step 2: Customize YouTube comment scraping workflow

Octoparse will automatically create a YouTube comments crawler for you.

You can make changes with the Tips it given to check all data you want can be found in the preview table. You can quickly delete any fields that you do not wish to scrape or rename.

Step 3: Scrape YouTube comments and download

Click the Save button in the upper-right corner to save the task you created in Octoparse. After that, hit the Run button and wait until Octoparse finishes its job. You can download the scraped YouTube comments in your desired format.

You can also scrape other information from YouTube channel, reading this article: How to Build A YouTube Channel Crawler, or watching the video guide below to learn more details.

Video guide to scrape YouTube video info with Octoparse

Method 3: Scrape YouTube Comments with Python

Python scripts provide another powerful way to scrape YouTube comments. But, this method is complex if you do not have computer programming experience.

Note: Make sure you have a Python environment setup on your computer before proceeding with this method.

How to scrape YouTube comments using Python programming language

Step 1: Install Google Chrome on your computer.

Step 2: Once done, install the ChromeDriver by clicking here.

Step 3: In your Python code, import the following libraries needed to perform YouTube comments data scraping.

import time
from selenium.webdriver import Chrome
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

These libraries are needed to crawl through YouTube comments that are dynamically loaded. Don’t worry if you don’t know precisely the purpose of every library used in the process.

Step 4: Add the below code to scrape YouTube video comments.

data= []
youtube_video_url= "https://www.youtube.com/watch?v=kuhhT_cBtFU&t=2s"

with Chrome(executable_path=r'C:\Program Files\chromedriver.exe') as driver:
    wait = WebDriverWait(driver,15)
    driver.get(youtube_video_url)
 
   for item in range(200):
        wait.until(EC.visibility_of_element_located((By.TAG_NAME, "body"))).send_keys(Keys.END)
        time.sleep(15)


    for comment in wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, "#content"))):
        data.append(comment.text)

Here, you have created a Python loop that will loop through the comments of your YouTube video and, during each iteration, append the scraped comments in the data field. Also, change the YouTube video link to your desired video from the youtube_video_url variable.

Step 5: Visualize the scraped YouTube comments using Python’s Pandas library. You need to add the following lines of code at the end of your python file.

import pandas as pd
df = pd.DataFrame(data, columns=['comment']
df.head()

What You Can Do With Exported YouTube Comments

Here’s how to transform your scraped YouTube comments into business intelligence.

1. Sentiment Analysis (Positive vs Negative Feedback)

The most immediate application is understanding how people feel about the content.

Export your comments to tools like MonkeyLearn, Google Sheets with sentiment analysis add-ons, or Python libraries like VADER to automatically classify comments as positive, negative, or neutral.

For example, if you’ve scraped 5,000 comments from a product launch video, sentiment analysis reveals whether the reception is overwhelmingly positive (65% positive comments suggests success) or divided (40% negative might indicate messaging problems). This gives you quantifiable data instead of gut feelings.

2. Identify Common Themes and Pain Points

Use word frequency analysis to find patterns. Tools like Voyant Tools or simple Python scripts can highlight which words appear most often in your comment dataset.

If “shipping delay” appears 200 times across comments on your product videos, you’ve identified a customer service issue that needs addressing.

Look for clustering patterns: Are negative comments concentrated around specific topics? Are positive comments praising particular features? This thematic analysis guides product development and marketing strategy.

3. Analyze Multilingual Audiences

If you’re scraping comments from international creators or global brands, language barriers become your biggest challenge.

A video with 10,000 comments might have 40% in Spanish, 30% in Portuguese, and 20% in other languages—leaving valuable insights invisible if you only speak English.

When analyzing YouTube comments from international audiences, you’ll often encounter multilingual content. If you’re working with video content that needs translation, a specialized video translator can help you translate spoken content directly from videos, making it easier to understand and analyze comments in their original context.

4. When Do People Comment Most? (Best Time to Post Analysis)

If you’re scraping comments with timestamps, you can map engagement trends. Plot comment volume by hour to identify when your audience is most active. Analyze early comments (first 24 hours) separately from later comments—they often show different patterns, with early comments coming from dedicated subscribers and later ones from broader discovery.

This temporal analysis reveals content momentum: Does engagement drop off quickly (suggesting weak retention) or build gradually (indicating strong shareability)?

5. Identifying Your Most Engaged Viewers from Comment Data

Don’t just analyze comment text—look at who’s commenting. If your scraper captured usernames, you can identify your most engaged community members. Click through to analyze their profiles: What other channels do they follow? What content do they create?

This qualitative layer adds depth to quantitative sentiment scores. You might discover that negative comments cluster around a specific demographic (e.g., users from one region) or that your biggest advocates share specific interests (e.g., tech enthusiasts under 30).

6. How to Prioritize Which Comments to Respond To

Categorize comments by type: questions, complaints, praise, spam. This segmentation helps you allocate response resources efficiently. If 30% of comments ask the same question (“Where can I buy this?”), you need a pinned comment or description link. If 15% report a specific bug, your dev team has a prioritized fix.

Automated response triggers can even be built: If a comment contains “not working” + product name, flag it for customer service follow-up.

7. Use Youtube Comments to Analyzing Your Competitors

When scraping competitors’ video comments, you’re essentially reading their customer feedback for free.

Look for patterns in complaints (“Their shipping takes forever”) that represent opportunities for your business. Note which features get praised—these are table stakes you need to match or exceed.

Compare engagement rates: If their launch video got 500 comments but only 50 are substantive (the rest are spam or one-word replies), their community engagement is weak despite surface-level metrics.

8. Using Comment Data to Train AI Content Recommendations

Large comment datasets can train custom classification models. If you’re in a specific niche (e.g., fitness, gaming, beauty), thousands of comments about products in your category can train a model to automatically categorize future comments without manual review.

For example, a beauty brand could train a model on 50,000 scraped comments to automatically detect which product complaints are about packaging vs. formula vs. price, streamlining quality control workflows.

9. What Comments Tell You About Your Next Video Topic

Your comment data reveals what your audience wants more of. If tutorial videos generate 3x more comments than product showcases, you know which content format resonates. If comments frequently ask “Can you do a video about X?”, you’ve identified content gaps.

Track which videos generate the most comment engagement relative to view count. A video with 10,000 views and 500 comments (5% engagement) performed better than one with 50,000 views and 500 comments (1% engagement).

Final Thoughts

Now, you have learned how to export all YouTube comments with both coding and no-coding. The first is to use Octoparse‘s free online youtube comment scraper, and the second is by using Octoparse and design a workflow yourself, the last one is by using a Python script. However, you need to have some programming skills to scrape YouTube comments with Python.

Choose the one which can meet your needs better. If you want to check out Octoparse, feel free to sign up an acount and try the 14 days free trial:

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FAQs

1. How many data fields should I export from YouTube comments?

I recommned starting with the basics: author, author URL, comment text, date, and like count. Add replies, sentiment, and language only if you need deeper analysis.

2. Do I need to code to export YouTube comments?

Not necessarily. There are easy no-code scraper templates that you can find in Octoparse and free web scrapers online, plus a Python option if you want more customization.

Pick what fits your comfort level and volume.

3. What challenges should I expect and how can I mitigate them?

Expect dynamic loading and possible IP blocks.

I suggest you use tools with good pagination support, pace requests sensibly, and have a fallback (API or alternate method) if scraping runs into trouble.

That’s why I recommend Octoparse. It covers the full range: no-code online templates, no-code desktop workflows, with built-in pagination and sensible pacing, basically act as a human browsing a website. It also offers IP rotation and other anti-blocking features such as CAPTCHA solvers, plus export options if it runs into obstacles.