Machine learning, text analysis, and more
I am heading home from my third year of attending rstudio::conf! If you weren’t there, watch for the videos to be released so you can check out the talks; I know I will do the same so I can see the talks I was forced to miss by scheduling constraints. I love this conference, and once again this year, the organizers have succeeded in building an impactful, valuable, inclusive conference.
I am an enthusiastic proponent of using tidy data principles for dealing with text data. This kind of approach offers a fluent and flexible option not just for exploratory data analysis, but also for machine learning for text, including both unsupervised machine learning and supervised machine learning. I haven’t written much about supervised machine learning for text, i.e. predictive modeling, using tidy data principles, so let’s walk through an example workflow for this a text classification task.
Do you subscribe to the Data is Plural newsletter from Jeremy Singer-Vine? You probably should, because it is a treasure trove of interesting datasets arriving in your email inbox. In the November 28 edition, Jeremy linked to the Small World of Words project, and I was entranced. I love stuff like that, all about words and how people think of them. I have been mulling around a blog post ever since, and today I finally have my post done, so let’s see what’s up!
I remember the first time I saw a deep learning text generation project that was truly compelling and delightful to me. It was in 2016 when Andy Herd generated new Friends scenes by training a recurrent neural network on all the show’s episodes. Herd’s work went pretty viral at the time and I thought: via GIPHY And also: via GIPHY At the time I dabbled a bit with Andrej Karpathy’s tutorials for character-level RNNs; his work and tutorials undergird a lot of the kind of STUNT TEXT GENERATION work we see in the world.
At the beginning of this year, I wrote a blog post about how to get started with the stm and tidytext packages for topic modeling. I have been doing more topic modeling in various projects, so I wanted to share some workflows I have found useful for training many topic models at one time, evaluating topic models and understanding model diagnostics, and exploring and interpreting the content of topic models.