My boss asked me the other day if I could automate a Python notebook I wrote about analyzing the content of customer comments made about experiences with staff. I said sure. Me: What
I was preparing to give a talk for some important Veteran Advocacy Non-Profits here in DC the other day, and wanted to provide an easy to understand example of machine learning and classification.
I'm teaching myself TensorFlow and Keras. The entire principle of using data flow graphs and deep learning is really fascinating, but it's a learning curve for sure. I've been poking around the MNIST
In this final task, my goal is to predict the Amazon score (1 - 5) based on the reviews - a multiclass text classification problem. This post discusses the use of five pipeline
In Part I we did some exploratory analysis after using the TextBlob package to apply sentiment scores using its polarity method. We found the mean sentiment was around .24 and then spilt up
Now that we know a little about the extremes of perfectly positive and negative sentiment reviews, let's analyze some key features of the text. Here we'll allow a user to retrieve the top