Salience has three methods of categorizing text: query topics, concept topics, and document categories. I’ll be going through all three using a New York Times article called “China Says Former Security Chief Is Under Investigation“. Query […]
Classification: Queries vs. Models
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5 m
Classification is a few value proposition for text analytics – it allows users to quickly drill into articles of interest and look at trends over time. Setting up a classification scheme can be a lot […]
An Ode to Electronic Music
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3 m
I was classically trained in piano from about the age of five until I left for college – and pretty much hated every minute of it. If I wanted to hear the Moonlight Sonata, which […]
Tagging, Taxonomies, Categorization with Salience
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5 m
The world is your oyster… And if your world is data, Salience is your pearl. One of the things that makes this engine so great is its ability to categorize content automatically. It can tag […]
Porn Plan Pitfalls: What David Cameron Should Know About Classification
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5 m
David Cameron’s recent announcement of his plan to automatically block porn across all of England has certainly stirred up its share of controversy. When asked how exactly he planned to enact this filtration and what, […]
Salience 5.1.1 Feature Highlight: Automatic Categorization
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3 m
This is probably the coolest new thing that we have in our toolbox. All of our customers like the idea of “magic” functionality. We have a long tradition of providing functionality that is available and […]
Smartbrief uses Classification
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1 m
Here’s the fourth video taken from our LUG conference.Smartbrief is one of the first adopters of our Concept Topics wikipedia-based classification technology. In this presentation, Zack Mully discusses how concept topics help them sift through […]
The simplicity of configuring a concept topic.
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In about 6 minutes, I’ll show you how easy it is to configure a concept topic that classifies documents by two different classifiers:1) Is a country mentioned that is in the Middle East?2) Are there […]
Discovering relevant concepts in hotel reviews
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3 m
In an earlier post, Jeff Catlin described analysis that we did around Bally’s vs. Bellagio using publicly available customer reviews. We did this analysis using something called “categories”, which is basically a fancy name for […]
Twitter? Yes, we can analyze that…
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2 m
If you’ve got tweets, we’ve got sentiment. And themes. And most mentioned people. And spam lists. In fact, the only issue we’ve run into is that Twitter won’t give us all the data to analyze. […]
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