Methodology
This page explains how RedDB turns raw Reddit data into the product rankings, sentiment scores, and feature breakdowns you see on the site.
1. Data collection
We search Reddit for product mentions across relevant subreddits. For a category like headphones, that includes communities like r/headphones, r/audiophile, r/HeadphoneAdvice, and dozens of other forums where people discuss audio gear. We use Reddit search to find posts and comments that mention specific products, then verify each mention to ensure it is genuinely about the product rather than a passing reference.
Our database currently contains over 300,000 verified mentions across more than 24,000 subreddits. We periodically refresh this data to capture new discussions.
2. Relevance and opinion filtering
Not every mention of a product is useful. A comment saying “I just ordered the Sony WH-1000XM5” tells us nothing about the product’s quality. We use large language models to classify each mention on two dimensions: whether it is relevant to the product, and whether it contains an actual opinion. Only mentions that pass both checks make it into the analysis.
3. Sentiment analysis
Each relevant, opinionated mention is scored for sentiment. Rather than a simple positive/negative binary, we analyze the nuance of each comment: what specific aspects are praised or criticized, how strongly the opinion is expressed, and what context surrounds it. The sentiment score for each mention feeds into the overall product sentiment breakdown shown on product pages.
4. Feature extraction
Beyond overall sentiment, we extract which specific features people discuss. If a headphone review praises noise cancellation but criticizes comfort, those are recorded as separate feature-level data points. The features on product pages are surfaced from this extraction and ranked by discussion volume and sentiment direction.
5. Scoring and ranking
The Reddit Score runs from 0 to 100, with 50 as the neutral starting point. It combines the sentiment of relevant opinions with evidence volume, Reddit votes, and recency. A product with 50 positive mentions out of 60 will score higher than one with 5 out of 6 even though the percentages are similar, because more data points provide a more reliable signal. The number of opinions shown beside the score is important context, especially for products with limited coverage.
To avoid counting the same person more than once, scoring uses one relevant opinion per author per product: their highest-upvoted mention. The displayed opinion count and overall sentiment breakdown include all analyzed relevant opinions, so the opinion count can exceed the number of authors used for scoring. The sentiment bar shows the distribution of opinions, not the Reddit Score out of 100.
Rankings within each category are derived directly from these scores. We do not manually adjust rankings, accept paid placements, or boost products based on affiliate revenue.
Category rankings only include products we currently consider in stock. Temporarily unavailable products can still retain product pages and appear in comparisons, but they are excluded from ranked lists until they return to stock.
6. Limitations
Reddit’s user base skews toward specific demographics, so some categories may contain biases that do not reflect the general population. Budget products may receive less discussion than enthusiast-grade gear. We show the mention count on every product page so you can judge the sample size yourself.
Sentiment analysis, even with modern language models, is not perfect. Sarcasm, context-dependent opinions, and multi-product comparisons can sometimes be misclassified. We show the original Reddit mentions alongside our analysis so you can read the source and form your own judgment.
7. Updates
Data is refreshed periodically as new Reddit discussions are collected. Product prices are updated from Amazon. Scores and rankings are recomputed after each data refresh. This page will be updated if we make significant changes to our methodology.