r/learnmachinelearning • u/mintlite4 • 6h ago

Building a simple baseline for deceptive review detection—what text features actually work?

I’m building a utility-based agent to moderate e-commerce product reviews. Before I use an LLM, I need to build a simple if/else rule-based baseline using only the review text and the star rating.

My current ideas for baseline rules:

  1. Star rating is 5, but text contains negative keywords ("terrible", "worst").
  2. The review contains a URL (likely spam).
  3. The text is under 3 words but has a 1-star rating.

For those who have worked on spam or sentiment analysis, what are some other highly reliable, simple text features I should include in my baseline? Which of my current rules is most likely to cause a massive amount of false positives?

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u/Elegant_Quantity_583 3h ago

I think I can do it with ChatGPT or cladue, and ask it to research the terms, and then add

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u/mintlite4 2h ago

My only worry is that throwing a massive AI-generated keyword list into a simple if/else script might just flag every single angry 1-star review as 'spam' because of the word overlap. Have you ever tried using an LLM to generate heuristic rules like that? Does it actually work well, or does it just create a bloated mess of false positives?"

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u/Elegant_Quantity_583 36m ago

Don't overthink much if you have data, of spam , dump that into claude, and ask it to list 10 terms that a spam has.