SemanticHacker classifier
Wandora's SemanticHacker classifier requests a text document and classifies it using SemanticHacker's Simplified Semantic Signature. The effect is that given text document or fragment is tagged with several tags possibly reflecting the content of the text. However, tags and tag weights provided by the SemanticHacker should be considered as an entity signature of the text. This is little different perspective compared to OpenCalais classifier for example.
SemanticHacker classifier requires valid api token from SemanticHacker. You can apply your token here.
SemanticHacker classification example
SemanticHacker classifier starts with menu option File > Extract > SemanticHacker classifier.... Let's try to classify WandoraWiki's first text chapter.
Before classification Wandora requests SemanticHacker api key used to access the web api. Wandora does not store the key between use sessions.
SemanticHacker classification example continued
Although it was expressed above that SemanticHacker classification should be considered as a single signature entity, you might find it useful to chop down SemanticHacker dimensions. By default SemanticHacker dimension is a slash separated keyword list and one could assume keyword chain respects superclass-subclass relation i.e. left side keyword can be seen as a superclass of right side keyword. Next screenshots demonstrate how you can postprocess dimension keywords provided by the SemanticHacker. However, split operation is not SemanticHacker specific but can be used in any context where you need to split down topics to a chain of superclass-subclass topics.
Note: Regular expression used to split base names is a combination of back slash and slash character. Yes, it looks like V but isn't!
Additional notes
- SemanticHacker classifier accepts only texts less than 1000 characters.
