Rachael Tatman on the trouble with sentiment analysis

When I first got into NLP and discovered the problem of sentiment analysis, I was stoked. Finally, I can review text for sentiment and apply this to a work use case! I even suggested it to my boss at the time but it never came to fruition which, in hindsight, might have been a good idea. Later, I found out that sentiment analysis is sketchy at best and my new favourite NLP person Rachael Tatman explained why in a recent blog post:

You should almost never do sentiment analysis.

Thanks for reading, hope that cleared things up. 🙂 In all seriousness, though, the places where it makes sense for a data scientist or NLP practitioner working in industry to use sentiment analysis are vanishingly rare. First, because it doesn’t work very well and second, because even when it does work it’s usually measuring the wrong thing.

She then goes on to give examples of its problems, including an inability to decipher things like sarcasm. Vincent Warmerdam did a talk with Hugging Face last year where he discussed similar issues and highlighted its inherent flaws which I highly recommend. So yeah, think it through and then think some more before you try analysing sentiment. And then maybe don’t?

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