Investigating Audience Snacking Habits with Facebook Topic Data
You might have seen our recent blog post where we highlighted some surprising findings about snacking habits based on research using Facebook topic data. In this post we'll take a look at how the research was carried using our platform.
Testing long-held assumptions
We're all increasingly looking to make better data-informed decisions. In this case an agency working on behalf of a brand of popular snack was looking to improve the effectiveness of their advertising around the time of...
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Important Announcement on API Versioning
As the DataSift platform continues to develop so does inevitably our API. As we have a full roadmap of new products and features coming up this feels like a good time to clarify how we version our API and how you can best keep up with the changes.
You'll see below that we're planning to deprecate a couple of API versions over the coming months, so please take a moment to read about the changes.
We've created some new resources to help you stay up-to-date....
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Validating Interaction Filters with Facebook Super Public Text Samples
DataSift PYLON for Facebook Topic Data allows you to analyze audiences on Facebook whilst protecting users' privacy. To help you build more accurate analysis we're introducing 'Super Public' text samples for Facebook. You can use Super Public text samples to validate your interaction filters to check you are recording the correct data into your index for analysis. You can also use these text samples to train machine learned classifiers. In this post we'll take a look...
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Introducing Keyword Relationship Models
Identifying and expanding on keywords and terms is a key challenge when filtering, classifying and analyzing text data. We're always looking at how we can make this challenge easier. One area we've been researching is finding relationships between words using word2vec.
Today we've released a tool which allows you to explore relationships between words. We've also created our first Keyword Relationship Model for you to explore. This model represents over three million unique...
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Building Better Machine Learned Classifiers Faster with Active Learning
You might have seen our recent announcement covering many things including the announcement of VEDO Intent. You're probably aware that DataSift VEDO allows you to run machine-learned classifiers. Unfortunately creating a high-quality classifier relies on a good quantity and quality of manually classified training data (which can be a painstaking task to produce) and exploration of machine learning algorithms to get the best possible result.
VEDO Intent is a tool that helps...
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