Blog/Tutorials

Reddit Sentiment Analysis in Python

Measure how Reddit feels about a product or topic: search posts, pull their comments, and score them with VADER in Python. Full script with CSV output.

The Social Parse team · · Updated

Reddit sentiment analysis means collecting what people say about a topic on Reddit and scoring each comment as positive, negative or neutral. In Python that's three steps: search Reddit for posts about the topic, fetch their comments, and score the comments with a sentiment model such as VADER. This guide builds all three and ends with a script that saves a scored CSV and prints a summary.

What you'll build

A script that takes a topic, say "framework laptop", and:

  1. finds the month's top Reddit posts about it,
  2. fetches the top comments of each post,
  3. scores every comment from -1 (negative) to +1 (positive),
  4. prints the share of positive, neutral and negative comments, and the strongest of each,
  5. saves everything to a CSV for a spreadsheet or a notebook.

Setup

Install the two packages:

pip install requests vaderSentiment

Get an API key (Social Parse gives new accounts 100 free credits) and set it with the base URL:

export API_KEY="your-api-key"
export API_URL="https://api.socialparse.dev"

VADER is a rule-based model tuned for social media text: it understands capitals, exclamation marks, emoji and slang, needs no training, and runs instantly on a laptop.

Step 1: find posts about the topic

The search endpoint returns posts from all of Reddit. sort=top with time=month gives the posts people engaged with most in the last month:

import os

import requests

API_URL = os.environ["API_URL"]
HEADERS = {"x-api-key": os.environ["API_KEY"]}


def get(path, params=None):
    response = requests.get(f"{API_URL}{path}", params=params, headers=HEADERS, timeout=120)
    body = response.json()
    if not response.ok:
        raise RuntimeError(f"{body['error']['code']}: {body['error']['message']}")
    return body


posts = get("/v1/reddit/search", {"q": "framework laptop", "sort": "top", "time": "month"})["data"]

To stay inside one community, use subreddit post search instead: /v1/reddit/subreddits/search?subreddit=framework&q=battery.

Step 2: fetch the comments

Post comments returns a post's comments; sort=top puts the most upvoted first:

comments = []
for post in posts[:10]:
    if not post["comment_count"]:
        continue
    for comment in get("/v1/reddit/posts/comments", {"post_id": post["id"], "sort": "top"})["data"]:
        if comment["text"]:
            comment["post_title"] = post["title"]
            comments.append(comment)

Each comment has its text, score (upvotes minus downvotes), depth (0 for a top-level comment) and created_at.

Step 3: score each comment with VADER

VADER's compound score runs from -1 to +1. The usual cut-offs are +0.05 and -0.05:

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

analyzer = SentimentIntensityAnalyzer()


def label(compound):
    if compound >= 0.05:
        return "positive"
    if compound <= -0.05:
        return "negative"
    return "neutral"


for comment in comments:
    comment["compound"] = analyzer.polarity_scores(comment["text"])["compound"]
    comment["sentiment"] = label(comment["compound"])

Step 4: summarize

counts = {"positive": 0, "neutral": 0, "negative": 0}
for comment in comments:
    counts[comment["sentiment"]] += 1

total = len(comments)
for sentiment, count in counts.items():
    print(f"{sentiment:>8}: {count:4} ({count / total:.0%})")

by_score = sorted(comments, key=lambda comment: comment["compound"])
print("\nMost negative:", by_score[0]["text"][:200])
print("Most positive:", by_score[-1]["text"][:200])

A raw count treats a comment with 2 upvotes the same as one with 2,000. To weigh agreement, add up each comment's max(score, 1) per label instead of counting it once.

The full script

import csv
import os
import sys

import requests
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

API_URL = os.environ["API_URL"]
HEADERS = {"x-api-key": os.environ["API_KEY"]}
analyzer = SentimentIntensityAnalyzer()


def get(path, params=None):
    response = requests.get(f"{API_URL}{path}", params=params, headers=HEADERS, timeout=120)
    body = response.json()
    if not response.ok:
        raise RuntimeError(f"{body['error']['code']}: {body['error']['message']}")
    return body


def label(compound):
    if compound >= 0.05:
        return "positive"
    if compound <= -0.05:
        return "negative"
    return "neutral"


def main(topic, max_posts=10):
    posts = get("/v1/reddit/search", {"q": topic, "sort": "top", "time": "month"})["data"]

    rows = []
    for post in posts[:max_posts]:
        if not post["comment_count"]:
            continue
        for comment in get("/v1/reddit/posts/comments", {"post_id": post["id"], "sort": "top"})["data"]:
            if not comment["text"]:
                continue
            compound = analyzer.polarity_scores(comment["text"])["compound"]
            rows.append({
                "post": post["title"],
                "subreddit": post["subreddit"],
                "comment_url": comment["url"],
                "score": comment["score"],
                "compound": compound,
                "sentiment": label(compound),
                "text": comment["text"],
            })

    counts = {"positive": 0, "neutral": 0, "negative": 0}
    for row in rows:
        counts[row["sentiment"]] += 1
    print(f"{len(rows)} comments on {topic!r}")
    for sentiment, count in counts.items():
        print(f"{sentiment:>8}: {count:4} ({count / max(len(rows), 1):.0%})")

    with open("sentiment.csv", "w", newline="", encoding="utf-8") as file:
        writer = csv.DictWriter(file, fieldnames=list(rows[0]) if rows else ["text"])
        writer.writeheader()
        writer.writerows(rows)


if __name__ == "__main__":
    main(sys.argv[1])
python sentiment.py "framework laptop"

What it costs

One credit for the search page, plus one per post's comments: ten posts cost 11 credits. Run it weekly on five topics and it's about 55 credits a week. Errors cost nothing. See Credits and pricing.

Making the analysis better

  • Sarcasm and domain words. VADER reads "this battery is sick" as negative. For more accuracy, swap in a transformer such as cardiffnlp/twitter-roberta-base-sentiment-latest from Hugging Face, or ask an LLM to label comments with a reason.
  • Aspects, not just tone. Count mentions of features ("battery", "keyboard", "price") in negative comments to see what people complain about.
  • Trends. Save each run with its date and chart the share of negative comments week by week.
  • More platforms. TikTok comments come in the same envelope, with a text field too: see the TikTok comments guide.

FAQ

What's the best Python library for Reddit sentiment analysis?

VADER is the quickest start: no training, fast, and tuned for social media. For higher accuracy on nuanced text, use a transformer model or an LLM.

Do I need Reddit API credentials?

No. Social Parse reads public Reddit pages with one API key: no Reddit app, OAuth or approval. See the Reddit API pricing guide for how the official API compares.

Can I analyze a single subreddit?

Yes: use subreddit posts (sort=top&time=month) or subreddit post search to collect posts, then the same comment and scoring steps.

How many comments do I need?

A few hundred comments give a stable read on a topic. Fewer than 50 and a single thread can swing the result.

Related posts

More from the Social Parse team.

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