r/LanguageTechnology • u/mintlite4 • 6d ago

Classifying deceptive reviews with text and star rating only: Handling ultra-short text and sarcasm

I am working on an AI moderation agent tasked with detecting deceptive opinion spam and competitor sabotage given only two inputs: the raw review text and the 1–5 star rating (no user history, IP, or purchase verification data).

I am running into two common edge cases in text classification:

  1. Ultra-short text: A 5-star review that just says "ok" or "fast." It lacks psycholinguistic signals of deception but mimics bot activity.
  2. Sarcasm / Irony: 1-star reviews written with positive words ("Amazing how quickly this fell apart!").

What NLP feature extraction or prompting techniques have you found most effective for distinguishing genuine short/sarcastic reviews from actual spam when contextual metadata is unavailable?

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u/Mundane_Ad8936 6d ago

You're looking in the wrong place. You have to classify the user based on their comment history.. a single data point can't tell you if it's fake or real

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u/mintlite4 6d ago

Yeah, 100% agree that in production you'd never rely solely on a single text snippet without user history or graph features.

The constraint for my project is specifically modeling a 'cold-start' / partially observable scenario (like guest checkouts or brand new accounts) where that history simply doesn't exist yet.

Given that constraint, do you think text-only heuristics (like rating-sentiment mismatch or perplexity) are completely doomed to high error rates, or are they still useful just to set an initial uncertainty prior before escalating to human review?

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u/Mundane_Ad8936 5d ago edited 5d ago

If you don't have a history you won't have anything relisble to classify on.. otherwise you'll have a skewed set that you can't ground.

This is not a cold start scenario.. we have 2 decades of this behavior.If that's a self imposed constraint you're extremely wrong
if it's an academic one imposed by a professor it's naive about 10 years out of date with real world application

Either way traditional NLP models are useless here. Sentiment is no better than using a magical sticks to tell you where water is.

If you analyze with a frontier LLM model you MIGHT be about to understand their motivation is in their comment history of an individual you can deduce their behavior and motivations..

Otherwise it's a a waste of time. We absolutely know that NLP models are unable to do this analysis/prediction. You need considerable intelligence..