I finally caught myself relying on bad training data for my model

I spent 3 months building a sentiment analysis tool for restaurant reviews and kept getting 40% accuracy. Last week I realized I was using Yelp reviews from 2014 because they were free, but the language in reviews has totally changed since then. Has anyone else wasted time on old datasets before realizing you need current stuff?

2 comments

2 Comments

olivia_webb1mo ago

Oh man, that hits way too close to home. I spent a solid two months training a model on Twitter data from 2016 thinking I was being smart and saving money. Turns out people wrote way differently back then - like nobody was saying "lit" or "slay" in 2016 the way they do now, and my model kept labeling positive reviews as negative because it didn't understand modern slang. The part that really stung was when I finally looked at the dates and realized I could have tested on just a few hundred current posts first before committing to the whole old dataset. At least you caught it after three months instead of six like I did.

charlesowens1mo ago

Old data is a trap and it gets so many of us. What worked for me was building a tiny test set first, like 100 recent posts from the same type of content I wanted to analyze. Running that quick check showed me my old data was garbage before I wasted weeks training. Also started keeping a running list of new slang and phrases I saw popping up, just a simple text file on my phone. That way when I grab new data I have a quick way to check if the language is current enough. Cutting the big dataset down to just the last 12 months of reviews made a huge difference in accuracy for me. It is annoying to have to pay for newer data sometimes but the time saved in training and fixing mistakes is worth it.