You already know that a decision that is backed by some analysis and data is usually better than a decision that comes purely from the gut. So when you need to make a decision on ways of improving your customer success experience, you need some data to work with. Most organizations employ some twitter usage in their customer success, some more than others. That’s why it’s important to do some analysis on incoming tweets and how well they are replied to.
This blog post was originally posted on MonkeyLearn by Rodrigo Stecanella
Since the beginning of the brief history of Natural Language Processing (NLP), there has been the need to transform text into something a machine can understand. That is, transforming text into a meaningful vector (or array) of numbers. The de-facto standard way of doing this in the pre-deep learning era was to use a bag of words approach.
In 2016 Amazon inaugurated the Alexa Competition dedicated to “accelerating the field of conversational AI”, with the winner to be determined in late 2017. As part of the competition, university research teams attempted to build a socialbot that could converse coherently and engagingly with humans on popular topics for 20 minutes.