AI-Powered Content Classification from FUSE Topic Modeling™ AI
Get Discovered.
You may have a list of taxonomy (e.g. topics, classifications, tags) that are meaningful to your users.
Many organizations are stuck, however: years and years worth of valuable content with varying degrees of classification applied, inconsistent or different classification across content sources, or no classifications at all.
If you have hundreds, thousands, or hundreds of thousands, of unclassified content residing on one or more of your content sources, then FUSE Topic Modeling™ AI, powered by state-of-the-art machine learning models, is for you.
Check out our blog post about some of the benefits of dynamic content tagging.
How It Works
In short, step one is to train the topic modeling engine with some sample content that is already classified to your specification. Once this is done, FUSE Topic Modeling™ AI takes over. After your content gets synchronized and indexed, the topic modeling engine runs and assigns classifications per your taxonomy model.
Frequency
You can run the topic modeling process one-time — helpful in situations where you need to classify historical content and you plan to self-classify moving forward — or you can run it continuously taking the pressure off of content editors to classify content correctly. Ongoing classification can also be helpful for user- or volunteer-contributed content when staff doesn’t have a hand in it otherwise (e.g. community posts).