As we honor Mental Health Month, there are many calls to reduce suffering. Seems reasonable, right? It’s even in California’s Mental Health Services Act (MHSA), where public systems are called to “reduce subjective suffering.” And as we broadly focus more on outcomes in health, measuring suffering (and hopefully its reduction)
This blog shows how the automatically generated concepts and categories in Visual Text Analytics (VTA) can be refined using LITI and Boolean rules. I will use a data set that contains information on 1527 randomly selected movies: their titles, reviews, MPAA Ratings, Main Genre classifications and Viewer Ratings.
In this blog, I use a Recurrent Neural Network (RNN) to predict whether opinions for a given review will be positive or negative. This prediction is treated as a text classification example. The Sentiment Classification Model is trained using deepRNN algorithms and the resulting model is used to predict if new reviews are positive or negative.
