Reddit Sentiment Dashboard (Posts + Comments)

This dashboard summarizes daily Reddit activity and a dictionary-based sentiment score computed from text. The score increases when posts/comments contain more “positive” words (e.g., good, great) and decreases when they contain more “negative” words (e.g., scam, rugpull). It is best used for broad trends, not precise measurement.

Sentiment summary (averages)

  • Overall average sentiment (combined): -0.108 (computed over days with at least 1 post/comment)
  • Overall average sentiment (posts): -0.174
  • Overall average sentiment (comments): -0.186
  • Average sentiment on/after Dec 4, 2024: -0.108
Note: averages are sensitive when there are very few items on a day. Treat them as a “rough temperature check,” not a precise statistic.
Date range (UTC)
2024-12-04 → 2025-05-06
All dates present in the provided CSVs
Total posts
19
Rows in posts CSV
Total comments
105
Rows in comments CSV
Activity coverage
22/154 days
14.3% of days in-range have ≥1 post/comment
Median items on active days: 2

How to read this

  • Dec 4, 2024 is marked with a dashed vertical line because it’s the day $HAWK launched.
  • In your narrative, Dec 4 also represents the period where (reportedly) within hours, large early holders sold off, triggering the rug pull. Because this dashboard aggregates by day, those hour-level shifts show up as changes across dates rather than minute-by-minute.
  • A score near 0 means the text is neutral or contains few words from the sentiment dictionary.
  • Big jumps often happen on days with few items (small sample sizes), so treat spikes cautiously.

Dataset limitations (important)

  • Sparse data: This dataset covers 154 days, but only 22 days contain any posts/comments. Many dates have no observable discussion in these files.
  • No true “before” window (in these files): Days before Dec 4, 2024 contain 0 items here. Any “before/after” comparison is limited by what was collected.
  • Selection bias: Results reflect the specific subreddits/keywords used when collecting data, not all of Reddit (and not other platforms like X/Twitter, Discord, Telegram).
  • Text sentiment is approximate: Dictionary methods miss sarcasm, memes, slang, and context (e.g., “this is sick” can be positive, but may be scored negative/neutral).
  • Engagement ≠ representativeness: High-score comments/posts can amplify certain voices; the loudest opinions aren’t necessarily the majority.
  • UTC timestamps: Dates are grouped by UTC; activity near midnight can shift into the “next day” relative to U.S. local time.
Tip for your write-up: describe results as “signals present in the collected sample”, not definitive truth about everyone’s sentiment.

Sentiment trends over time

How sentiment is computed

  • This analysis uses a dictionary-based (lexicon) sentiment method, not a machine‑learning model.
  • Each post or comment is broken into individual words and compared against a predefined list of positive (e.g., good, great, profit) and negative (e.g., scam, rugpull, fraud) terms.
  • Words found in the dictionary contribute positive or negative scores; words not in the list contribute nothing.
  • Scores are lightly normalized so that longer posts do not automatically appear more extreme than shorter ones.
  • Daily sentiment values represent the average score of all posts and comments made on that day.
This approach is intentionally simple and transparent. It works well for identifying broad shifts in tone, but it cannot understand sarcasm, memes, or nuanced financial language.

Daily volume

Process & platform notes

  • Platform: Reddit posts (title + selftext) and comments (body), aggregated by day.
  • Sentiment method: A simple word-score dictionary. Each text is tokenized, matched against a small lexicon (examples: good, great, scam, rugpull), and normalized so longer text doesn’t automatically dominate.
  • Daily sentiment: The dashboard reports the average sentiment per day for posts, comments, and a combined metric weighted by how many items exist that day.
  • Why Reddit can look “bursty”: Attention often clusters around news drops, influencer posts, investigations, or big price moves—so discussion appears in spikes rather than steadily.
Coverage snapshot (posts):
  • CryptoCurrency: 16
  • CryptoMoonShots: 2
  • solana: 1
Coverage snapshot (comments, based on each comment’s post subreddit when available):
  • CryptoCurrency: 105