How to Scrape YouTube Comments with Python for Sentiment Analysis - Easy Guide

Hey FunWithAI.in fam! It’s me again – your data-crazy buddy who can’t resist a challenge. Last night, I got hooked on The Ultimate AI Battle! (https://www.youtube.com/watch?v=cMuif_hJGPI) – a video with 9,349 comments screaming for analysis. I wondered – which AI is the crowd favorite? ChatGPT? Grok? Claude? So, I grabbed my Mac, opened VS Code, and used the YouTube Data API to scrape 1,000 comments. After some trial and error, I crowned a winner! Bookmark this, share it with your tech crew, and join me on this personal journey – I’ve got code, screenshots, and a fun twist to keep it lively.

Table of Contents

Why I Dove into This AI Face-Off

I’ve been obsessed with data for marketing ever since my YouTube Shorts post (you guys went wild for that one!). The comments on this AI battle video felt like a treasure trove to tap into audience vibes. I remembered how sentiment analysis – figuring out if comments are happy, sad, or neutral – helped me before. Was ChatGPT ruling the roost, or would Grok surprise me? I had to know!

Step 1:

Before I even touched the API, I needed a solid coding space. I downloaded VS Code on my Mac – a quick drag from the official site. I opened it, created a folder called “youtube-comments-scraper” in ~/Downloads, and set it as my workspace. Took me a bit to figure out the Terminal inside VS Code – hit “Terminal” > “New Terminal” – but once I did, it felt like home.

Step 2:

Getting the YouTube API GoingFirst, I needed those comments. The YouTube Data API was my go-to. Here’s how I set it up, learned from past stumbles:

I jumped into Google Cloud Console, made a project called “YouTube Comments Extraction,” and turned on the YouTube Data API v3.
Grabbed an API key under “Credentials” – and kept it safe after that GitHub leak fiasco years ago!

Step 3:

Next, I made sure Python was ready. I checked my Terminal with “python3 –version” – got 3.9, phew! If it hadn’t worked, I’d have grabbed it from python.org. I learned from a past project that skipping this step leads to a headache, so I double-checked.

Step 4:

With Python set, I needed libraries. In the Terminal, I activated a virtual environment with “python3 -m venv .venv” and “source .venv/bin/activate” – took me a few tries to get that right after a Conda mix-up! Then, I ran “pip install google-api-python-client textblob pandas” – one failed at first, so I installed them one by one. Lesson learned: patience pays.

Step 5:

Pulling 1,000 CommentsWith the API ready, I coded in Python to grab comments. The API gives 100 per call, so I looped to hit 1,000. Check this out:

from googleapiclient.discovery import build
import re
from textblob import TextBlob
import pandas as pd

api_key = "YOUR_API_KEY"  # Replace with your key

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

video_id = "cMuif_hJGPI"

data = []
next_page_token = None
max_comments = 1000

while len(data) < max_comments:
    comments_request = youtube.commentThreads().list(
        part="snippet",
        videoId=video_id,
        maxResults=100,
        pageToken=next_page_token
    )
    comments_response = comments_request.execute()

    for item in comments_response["items"]:
        comment = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"]
        cleaned_comment = re.sub(r'<.*?>', '', comment).strip()
        sentiment = TextBlob(cleaned_comment).sentiment.polarity
        data.append({"comment": cleaned_comment, "sentiment": sentiment})

    next_page_token = comments_response.get("nextPageToken")
    if not next_page_token or len(data) >= max_comments:
        break

df = pd.DataFrame(data[:1000])  # Limit to 1000
df.to_csv("ai_battle_comments.csv", index=False)
print("Saved to ai_battle_comments.csv with", len(data), "comments")

It took a few tries – I hit the API limit once – but I got my 1,000 comments with sentiments!

Step 6:

Checking the ResultsI opened the CSV in Numbers and tagged comments mentioning AIs – ChatGPT, Grok, Claude – with “Yes.” After crunching 1,000 entries, here’s my haul:

ChatGPT: 93 positive mentions, avg 0.15
Grok: 87 positive mentions, avg 0.13
Gemini: 73 positive mentions, avg 0.21

Step 7:

Cleaning comments wasn’t automatic – I built a function for that. I messed around with regex in re.sub to ditch HTML tags, then added .strip() for spaces. Tested it on a few comments – saw “<b>Great</b>” turn to “Great” – and tweaked until it felt solid. It’s a small win, but it saved me later.

Step 8:

Crowning the Winner:

I built a dashboard in Numbers to tally it up. ChatGPT won with 93 positive vibes, though Gemini’s 0.21 avg showed some serious love. As a marketer, I’d go with ChatGPT’s popularity – it’s the crowd’s pick!

My Stumbles and Tips

I smashed into the API quota early – start with 100 next time!
TextBlob missed some slang – I’m testing VADER next.
Tagging AIs was manual; I’ll automate it soon.

Try It Out

Swap the video ID and use my code. Test a game vid or gadget launch – see what wins! Share your results on FunWithAI.in.

Spread the Word

Loved this? Tweet it, bookmark it, or comment below. Got another video to try? Let’s chat!

How to Scrape YouTube Comments with Python for Sentiment Analysis – Easy Guide