Andrew Larimer

One of the most useful ways to deploy Natural Language Processing at the moment is to uncover meaningful structure within unstructured text. While we’re nowhere near true machine comprehension of text, in this series of posts applying NLP to tweets about the NBA Finals, we’ve demonstrated a variety of techniques to extract meaningful data from text: sentiment analysis, topic clustering, numerical analysis of trends, time series analysis, etc. In this post, we want to look at another kind of structure we can often pull from text: the network graph represented by co-mentions of players & coaches.

The Raptors are back on top after the NBA Finals Game 3, and just as with the last couple of games: we want to know what people are saying about it. We’ve looked at Twitter’s reactions to the last few games through the lens of sentiment analysis on its own, but today we’re adding a new tool from the Natural Language Processing (NLP) toolkit: topic clustering.

In our last post, we saw that during Game 1 of the NBA Finals, the Raptors win saw more of a drop in sentiment for the Warriors than a spike in sentiment for the Raptors. With last nights’ Warriors win, we see some interesting trends that question that pattern.

Following up on SpringML’s Game of Thrones Twitter Sentiment Analysis, we’ve decided to do a streaming sentiment analysis pipeline during the NBA Finals to monitor NBA fans reactions on Twitter. We set up the pipeline the same way as the Game of Thrones sentiment analysis.

Whether or not you’re a fan of the show, given a moderate amount of social media exposure over the past week, you probably noticed that Game of Thrones viewers’ emotions were running high as we headed into the show’s final episode. I was curious how fans would react to whatever ended up happening Sunday night, so I prepared a streaming sentiment analysis pipeline to keep a watch on Twitter to find out.