Introducing IBMsentR, a simple R package for textual sentiment and tone analysis through IBM Watson.
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The big cloud players — Amazon, Google, IBM, and Microsoft — have all put their machine learning tools at your disposal. But figuring out how much they cost, and how those costs compare, can be a great mystery. In this article we’ll decipher the cost structures of the four biggest cloud players across a variety of scenarios.
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We wrote an article earlier this year comparing 3 popular cloud sentiment analysis services. In this follow up piece we take a look at how Microsoft Azure Text Analytics stacks up.
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In the last 18 months, we’ve seen a major leap forward in the quality, accessibility, and cost of cloud-based sentiment analysis services.
So how do the big cloud sentiment services compare out of the box?
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Generally speaking, McKinsey gives good advice — better than Gartner, who have unhelpfully predicted that AI will create $1.2 trillion of “economic value” in 2018. In the case of McKinsey’s “Notes from the AI Frontier”, however, they have combined some useful thinking and case studies with an irresponsible presentation of market projections.
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It will surprise no one to learn that airport travelers tend to be wealthier and more educated than the general population. But here’s the interesting part: three quarters of frequent flyers say that they actually pay attention to advertising in the airport.
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Brand marketers have jumped at the chance to do something with the Alexa Skills platform, but have struggled mightily. To this point, they are almost all failures: Sloppy user experience, poor branding, and limited usefulness abound.
Why have so many brands failed? One simple fact: they approaching Alexa with too much advertising baggage.
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If you run marketing campaigns on Facebook, you might be frustrated by their limited built in reporting — or reliant on massive Excel spreadsheets to do your campaign analysis.
We decided to build a package for our favorite data science tool — R Studio — that pulls Facebook campaign insights directly from their marketing API.
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R isn’t just a tool for statistical analysis any more. It’s also a powerful platform for creating programmatic advertising campaigns.
We decided to dive deep into Facebook's marketing API to automate the creation of Canvas ads, using R Studio as the backend for creation and analytics. Here's what we learned...
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Google Affiliate Location Extensions have huge potential to drive offline sales. They make it possible for a manufacturer to advertise directly to active shoppers near a retail location that carries their product.
But if your product isn’t available at a national chain like Target or Costco, you are out of luck. We have a solution.
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