Something That Bugs Me About User Reviews

If you have been on Yelp and other sites that review places or things, like Amazon product reviews, don’t you hate the users who determine their rating points based on a minuscule aspect of the restaurant, product, or whatever? Like for example, people who give restaurants with amazing food one star simply because they couldn’t find parking? Or they slammed a kitchen appliance because it didn’t come in a color that they liked. One wonders if there was some way to develop an algorithm that could remove the scoring on reviews that slammed something due to a non-core attribute being rated low. I guess that is one of the issues with crowdsourced ratings and reviews; how do you keep the quality level of a rating consistent. If I write a review, it’s usually based on promoting a place/product that I’m going to/using to others or dissuading them from going to/purchasing it. But as I look over both Yelp and Amazon reviews, especially the negative ones, they are rife with ratings crushing complaints about minor aspects – at least in my opinion.

I’m guessing that this is not a trivial problem – unless you can map the data points and give the user a specific set of attributes to rate on, how can you get a clear sense of the ratings of a place/thing with all of the extraneous mumbo-jumbo removed. Something to think about – maybe a startup focused on scrubbing the set’s unimportant data would be interesting.