by Scott D'Angelo, Zia Mohammad | Updated September 18, 2018 - Published September 19, 2018
Artificial intelligenceData scienceNatural language processingNode.js
Not to be mistaken for the canned meat, these unwanted texts, tweets, and emails that we receive come at a cost. Enterprises around the globe deal with spam messages. The Radicati Research Group stated that spam will cost a business $20.5B, annually, due to decreased productivity. Although traditional spam classifiers exist, the key differentiation required for enterprise solutions is scalability and the ability to own your own data.
Because we know about spam and have email examples, we can use AI tools to help us determine the nature of email content. IBM Watson Natural Language Classifier (NLC) is a perfect fit for this use case. By providing the training data, we give the NLC service all that is needed to determine which emails are spam and which aren’t (which we label “Ham”). The convenient drag-and-drop GUI makes creating the model simple. Then, you can use Watson Developer Cloud SDKs to integrate the service into your application.
Use the open source code to run through this code pattern and quickly get up to speed. You can adapt this code for your own purposes and use other Watson Services to solve your complex problems with simple-to-use AI tools.
April 23, 2019
Artificial intelligenceData science+
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