Over two hackathons, I have created two Amazon Alexa Skills that integrate with Expert.ai textual analysis APIs. Source code is available on GitHub (see links below).
Sentiment Expert is an Amazon Alexa skill that uses Expert.ai sentiment analysis to measure sentiment in the user's utterance. Rather than just providing raw measurements, Sentiment Expert challenges the user to match target measurements for positive and negative measurements, with higher scores awarded for proximity to the targets.
Sentiment Expert was built as an Alexa voice interaction model (a set of JSON files) with an AWS Lambda back end written in Node.js. The back end code makes calls to the Expert.ai sentiment analysis API.
Typical Topical is a demonstration game that challenges you to say things that meet a target you are given for topic, behavior type and emotion type. You score based on how closely you meet those targets. The skill uses textual analysis from Expert.ai to detect and determine the topic, the behavior described and the emotion expressed in the statements you make.
I used the Alexa Skills Kit SDK, creating interaction models in JSON and back-end processing as an AWS Lambda function using Node.js. I use the list of Expert.ai-detectable topics, and the hierarchies of behavioral traits and emotional traits to generate targets for the user to meet. After receiving a user utterance from Alexa, I send the utterance in parallel to three Expert.ai NL APIs: analyze to detect a topic, categorize/behavioral-traits to detect behavior, and categorize/emotional-traits to detect emotions. Scoring is based on the ability of the user to utter a statement that meets the targets.