There are two possible ways to resolve a false negative, depending on the type of model we are working with and on the scenario we have:
There are four things you can check:
This option can be applied to hybrid models and to rule-based one.
It's important to remember than hybrid models carry out the statistical classification first, and then over the results, they apply the rule-based classification. If one of the terms defined in the rules does not appear in the training text, it's possible than a text will not be classified in that category in the statistical classification, and so it will not appear in the final results either.
To ensure that this does not happen, it's recommended to check that all the positive and relevant terms associated to a category appear also in the training text of the category, adding them if they don't.
The way of resolving a false negative using training text is just to add the text that's given the false negative to the training text of the category in which it should be classified.
This option can be applied to hybrid models and to statistical ones.
It's important to remember than modifying any category may change the relevance values the model assigns to the rest of the categories.