amazon
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 tutorial, and tried my hand at hacking some TensorFlow scripts like
textblob
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 the dataframe into five calendar years of review from 2008 to 2012, getting a better sense of
NLP
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 25 text features based on inputting an Amazon Score of 1 to 5
sentiment
Ok, so now that we explored the contents of the Amazon Fine Reviews data file, it's time to move on to the second task: How many reviews and products are perfectly positive and perfectly negative in sentiment? TextBlob assigns a polarity value from -1.0 to +1.0,
amazon
It takes time to take advantage of all the great data science/ML resources out there like Coursera, Udacity, HackerRank, and Kaggle competitions. I won't bore you with the details, but granted that life sometimes gets in the way, one of my goals is to get into Kaggle