I had high expectations for somehow incorporating IBM Watson‘s Natural Language Processing (NLP) into my quantum portfolio optimization project. My idea was to take the algorithm’s recommendation and then check the current news and views about each asset for additional consideration.

I was very surprised to discover that Watson uses the TextBlob Python library. I had been expecting to find a powerful, proprietary NLP engine. But, no, the engine is TextBlob.

I’ve already used TextBlob with my Google Web Scraper and with Twitter Sentiment Analysis. I prefer to use it in tandem with the VADER Sentiment Analysis library. If both libraries agree, that adds confidence to the result. If they disagree, that signals uncertainty.

I’ve run TextBlob on a cloud server. When analyzing large amounts of text, it’s slow. I’m sure Watson offers a noticeable performance boost, but I’m still surprised that the real workhorse is “off-the-shelf.”

I guess that’s a testament to how good TextBlob is.