How to Extract YouTube Comments in Python

Learn how you can monitor network traffic in the browser to write a Python script that extracts YouTube comments using requests library in Python.
  · 10 min read · Updated may 2022 · Web Scraping

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Being able to extract comments from the biggest video sharing website in the Internet is a handy tool, you can extract comments to perform tasks such as text classification, or you may want to extract comments of your YouTube videos to do certain tasks, the possibilities are endless.

In this tutorial, we will not only write a Python script to extract YouTube comments, but we will be experimenting with the network utility in browser's developer tools in order to capture the right comment request so that it can help us on writing the code.

Note: If the code of this tutorial doesn't work for you, please check out using YouTube API tutorial instead.

Related: How to Extract YouTube Data in Python.

Before we get started, let's install the requirements:

pip3 install requests

Monitoring Network Traffic in the Browser

Now in order to follow along with me, go into any YouTube video of your choice with Chrome or any other browser and right click and choose Inspect Element and go to Network section:

Monitoring Network traffic in the browserNotice I wrote "comment" in the filter input field, this will help us filter out the unwanted HTTP requests such as images, style and Javascript files, etc.

Now go to the video page and scroll down until you see some comments loaded. If you go back to the network tool, you'll see something like this:

A new comment request detected

Great, we have successfully captured a comment request, if you click on it, you will see the actual request URL, method and the remote IP address:

Comment request general detailsThat's good so far, remember the goal here is to simulate this HTTP request in Python, but since this is a POST request, we gonna need further details, such as the POST body and URL parameters.

If we scroll down to the bottom in the same section (Headers), we will see the query string parameters and form data, like so:

Comment Request DetailsAwesome, so we need action_get_comments, pbj, ctoken, continuation, itct and session_token parameters. Notice action_get_comments and pbj parameters should have the value of 1, and ctoken and continuation have the same value. In the next section we will see how we can extract them from the YouTube video page source code using Python.

Also, if you keep scrolling down to load more comments, you'll see similar requests getting added to the requests list:

2nd comment request detailsNotice some parameters changes their value on each comment loading request. Don't worry, we'll handle that in the next section.

Writing the Comment Extractor in Python

Now that we understand how the comment loading request is made, let's try to simulate it in Python, importing necessary modules:

import requests
import json
import time

Since we gonna need to parse some data (the parameters seen in the last section) from the content of the video page, we won't be using HTML parser such as BeautifulSoup, that's because most of the data lives in a Javascript object within script tags. As a result, the below two functions will help us search over content:

# from
def search_dict(partial, key):
    A handy function that searches for a specific `key` in a `partial` dictionary/list
    if isinstance(partial, dict):
        for k, v in partial.items():
            if k == key:
                # found the key, return the value
                yield v
                # value of the dict may be another dict, so we search there again
                for o in search_dict(v, key):
                    yield o
    elif isinstance(partial, list):
        # if the passed data is a list
        # iterate over it & search for the key at the items in the list
        for i in partial:
            for o in search_dict(i, key):
                yield o

# from
def find_value(html, key, num_sep_chars=2, separator='"'):
    # define the start position by the position of the key + 
    # length of key + separator length (usually : and ")
    start_pos = html.find(key) + len(key) + num_sep_chars
    # the end position is the position of the separator (such as ")
    # starting from the start_pos
    end_pos = html.find(separator, start_pos)
    # return the content in this range
    return html[start_pos:end_pos]

Don't try to understand them now, they will help us during the tokens extraction. Now let's define our core function that accepts a YouTube video URL and returns comments as a list of dictionaries:

def get_comments(url):
    session = requests.Session()
    # make the request
    res = session.get(url)

res has the HTTP response of the YouTube video web page, now let's get the session token from the returned HTML content:

    # extract the XSRF token
    xsrf_token = find_value(res.text, "XSRF_TOKEN", num_sep_chars=3)

The XSRF token is the session_token that is required in the form data in the request, if you view the page source of the video page and you search for it, you'll find it there:

Session tokenGreat, let's continue on extracting other fields, which are ctoken and itct.

The below line is responsible for extracting the Javascript object that has all the data we need:

    # parse the YouTube initial data in the <script> tag
    data_str = find_value(res.text, 'window["ytInitialData"] = ', num_sep_chars=0, separator="\n").rstrip(";")
    # convert to Python dictionary instead of plain text string
    data = json.loads(data_str)

Here is how it looks in page source:

YouTube Initial Data

Now data is a regular Python dictionary that contains all YouTube video data, now we need to search for nextContinuationData dictionary that has the required parameters:

    # search for the ctoken & continuation parameter fields
    for r in search_dict(data, "itemSectionRenderer"):
        pagination_data = next(search_dict(r, "nextContinuationData"))
        if pagination_data:
            # if we got something, break out of the loop,
            # we have the data we need
    continuation_tokens = [(pagination_data['continuation'], pagination_data['clickTrackingParams'])]

Going back to the page source, here is what we're searching for:

Dictionary keys to findSo we're interested in the continuation field (ctoken) and clickTrackingParams (itct)

The rest of the code makes a request to /comment_service_ajax to get and parse comments and gather the continuation tokens after each request we make, until there is no more comments:

    while continuation_tokens:
        # keep looping until continuation tokens list is empty (no more comments)
        continuation, itct = continuation_tokens.pop()
        # construct params parameter (the ones in the URL)
        params = {
            "action_get_comments": 1,
            "pbj": 1,
            "ctoken": continuation,
            "continuation": continuation,
            "itct": itct,
        # construct POST body data, which consists of the XSRF token
        data = {
            "session_token": xsrf_token,
        # construct request headers
        headers = {
            "x-youtube-client-name": "1",
            "x-youtube-client-version": "2.20200731.02.01"
        # make the POST request to get the comments data
        response ="", params=params, data=data, headers=headers)
        # convert to a Python dictionary
        comments_data = json.loads(response.text)
        for comment in search_dict(comments_data, "commentRenderer"):
            # iterate over loaded comments and yield useful info
            yield {
                "commentId": comment["commentId"],
                "text": ''.join([c['text'] for c in comment['contentText']['runs']]),
                "time": comment['publishedTimeText']['runs'][0]['text'],
                "isLiked": comment["isLiked"],
                "likeCount": comment["likeCount"],
                # "replyCount": comment["replyCount"],
                'author': comment.get('authorText', {}).get('simpleText', ''),
                'channel': comment['authorEndpoint']['browseEndpoint']['browseId'],
                'votes': comment.get('voteCount', {}).get('simpleText', '0'),
                'photo': comment['authorThumbnail']['thumbnails'][-1]['url'],
                "authorIsChannelOwner": comment["authorIsChannelOwner"],
        # load continuation tokens for next comments (ctoken & itct)
        continuation_tokens = [(next_cdata['continuation'], next_cdata['clickTrackingParams'])
                         for next_cdata in search_dict(comments_data, 'nextContinuationData')] + continuation_tokens
        # avoid heavy loads with popular videos

Perfect, let's test this out:

if __name__ == "__main__":
    from pprint import pprint
    url = ""
    for count, comment in enumerate(get_comments(url)):
        if count == 3:

This will extract the first 3 comments and print them out:

{'author': 'wizard yt',
 'authorIsChannelOwner': False,
 'channel': 'UCNg8yS4kYFvvkOQFIwR5NqA',
 'commentId': 'UgwiWPPBdLMwnBSCPwJ4AaABAg',
 'isLiked': False,
 'likeCount': 0,
 'photo': '',
 'text': 'Sub2sub pls i request you i want 50 subs',
 'time': '2 seconds ago',
 'votes': '0'}
{'author': 'Abdou Rockikz',
 'authorIsChannelOwner': False,
 'channel': 'UCA4FBhVyVNMO5LcRfJKwrEA',
 'commentId': 'UgzzD6ngnIFkLX_lnsx4AaABAg',
 'isLiked': False,
 'likeCount': 0,
 'photo': '',
 'text': 'This is a fake comment',
 'time': '4 seconds ago',
 'votes': '0'}
{'author': 'NIGHT Devil',
 'authorIsChannelOwner': False,
 'channel': 'UCA4FBhVyVNMO5LcRfJKwrEA',
 'commentId': 'UgxbTzFsW9wrD8qvuxJ4AaABAg',
 'isLiked': False,
 'likeCount': 0,
 'photo': '',
 'text': 'CLICK <hidden> for a video',
 'time': '6 seconds ago',
 'votes': '0'}

Finally, let's use argparse module to convert this to a command line tool that is usable by anyone:

if __name__ == "__main__":
    import argparse
    import os
    parser = argparse.ArgumentParser(description="Simple YouTube Comment extractor")
    parser.add_argument("url", help="The YouTube video full URL")
    parser.add_argument("-l", "--limit", type=int, help="Number of maximum comments to extract, helpful for longer videos")
    parser.add_argument("-o", "--output", help="Output JSON file, e.g data.json")
    # parse passed arguments
    args = parser.parse_args()
    limit = args.limit
    output = args.output
    url = args.url
    from pprint import pprint
    for count, comment in enumerate(get_comments(url)):
        if limit and count >= limit:
            # break out of the loop when we exceed limit specified
        if output:
            # write comment as JSON to a file
            with open(output, "a") as f:
                # begin writing, adding an opening brackets
                if count == 0:
                f.write(json.dumps(comment, ensure_ascii=False) + ",")
    print("total comments extracted:", count)
    if output:
        # remove the last comma ','
        with open(output, "rb+") as f:
  , os.SEEK_END)
        # add "]" to close the list in the end of the file
        with open(output, "a") as f:
            print("]", file=f)

This is a command line tool that accepts the YouTube video URL as required parameter, -l or --limit to limit the number of comments to extract and -o or --output to specify the output file in which the comments will be written as JSON, here is an example run:

$ python --limit 50 --output comments50.json

This will extract 50 comments from that video and write them to comments50.json file.

Note: If the code of this tutorial doesn't work for you, please check out using YouTube API tutorial instead.


By completing this tutorial, you're able to make a simple YouTube comment extractor script. Just to note though, a part of the code for this tutorial was brought from this repository.

if you want to download much more comments, I invite you to make a progress bar manually or using tqdm library, good luck doing that!

Check out the full code here.

Learn also: How to Extract YouTube Data in Python.

Happy Scraping ♥

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