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Reddit's favorite products, extracted using deep learning

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Reddit's favorite products, extracted using deep learning

Many people add "Reddit" to their search queries to find authentic product reviews. We fine-tuned a BERT model to extract product mentions from over 4 million Reddit comments and posts with Named Entity Recognition (NER). The result is a list of the most mentioned products across many subreddits. Soon, we'll roll out a version that includes sentiment (positive/negative mention). No platform (including Reddit) is resistant to fake reviews and spam, but we think it's happening less frequently here for various reasons: - Redditors and other forum members are more interested in boosting their ego by showing their depth of knowledge on the topic (and correcting others on the topic), whereas corporate websites are more interested in raking profit by displaying (potentially) dishonest information. - Enthusiasts in subreddits are pretty good at spotting dishonest or fake content, which results in immediate downvotes. The whole karma system helps with trustworthiness. - Most subs are moderated well and spam gets removed quite quickly That being said, good fake reviews are technically almost impossible to detect, even with sophisticated network analysis of the reviewer's profile.

Visit productView on Hacker NewsAug 31, 2022
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, using · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, reviews, soon · Missing: plus, intuitive, host
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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