How was Watson trained to understand both culture and music? The creative process was simple. To understand culture, AlchemyLanguage API first uncovered the most pervasive themes in each year. Then, Watson Tone Analyzer read blogs, news articles, and social media to gauge the sentiment around those themes. With this data, Watson arrived at an emotional fingerprint of culture.

We are pleased to announce the launch of a new Tone Analyzer endpoint trained for Customer Engagement scenarios. The new endpoint was trained on customer support conversations on twitter, and the tones included are frustrated, sad, satisfied, excited, polite, impolite and sympathetic. Currently, the new endpoint is Beta functionality in the IBM Watson Tone Analyzer service. Given a textual conversation between a customer and an agent or company representative, the service detects the above mentioned tones both from the customer's and the agent's text. Why Did We Build the Tone Analyzer for Customer Engagement Endpoint?

For this tutorial, we're going to power TJBot with APIs from Watson Developer Cloud. We'll start by putting a voice interface onto TJBot, then give it the ability to converse and understand your emotional tones. In part 2 of the tutorial, we'll transfer the code onto a Raspberry Pi and put the whole thing into the physical TJBot itself. For the sake of simplicity, we'll keep the conversation simple.

This column is a little cheerful, slightly analytical, both confident and tentative and just a tiny bit angry. At least that's what IBM's Watson thinks. Last week, IBM revealed that its Jeopardy-winning supercomputer has a new capability. It's called Watson Tone Analyzer. You can use it like spell check, except instead of checking your spelling, it checks the "tone" of your writing.