Add a step to normalise messages to the ML pipeline. This ensures
computed properties run on the raw data (which is actually partially
normalised by the compute context). This prevents properties which
rely on symbols (e.g. "B>") from being unable to work properly when
normalisation happens before they have access to the input.
Certain symbols are turned into one space so the model sees multiple
words instead of one. Previously "[RP]Hi" would turn into "RPHi" and
be its own token. Now it turns into "RP" and "Hi", counting as two
tokens. This change increased the model's accuracy.
Also make "18", "http", "https", and LGBT-related words into stop
words (meaning they're ignored). Each of these stop words made the
model more accurate and reduced unwanted bias.
Messages destined for ML are now normalised by the plugin in the same
way the model's input is for training. This should make the results
come closer to expected.
Hopefully no longer required the data structure to be updated when new computed properties are added. This should also reduce duplication and make it easier to make bigger changes to the model without needing to update the plugin.
