In the context of natural language processing, can anyone give a concrete example of True Positive, True Negative, False Positive, False Negative? Google post gives a interesting explanation about True Positive, True Negative, False Positive, False Negative


  True Positive (TP): Reality: A wolf threatened. Shepherd said: "Wolf."
  Outcome: Shepherd is a hero.
  
  True Negative (TN): Reality: No wolf threatened. Shepherd said: "No
  wolf." Outcome: Everyone is fine.
  
  False Positive (FP): Reality: No wolf threatened. Shepherd said:
  "Wolf." Outcome: Villagers are angry at shepherd for waking them up.
  
  False Negative (FN): Reality: A wolf threatened. Shepherd said: "No
  wolf." Outcome: The wolf ate all the sheep.


In the context of CV, the classifier predicts if an image contains cat

True Positive (TP): 
Reality: an image contains cat. 
classifier predicts: cat.

True Negative (TN): 
Reality: an image does not contains cat. 
classifier predicts: no cat.

False Positive (FP): 
Reality: an image does not contains cat. 
classifier predicts: cat.

False Negative (FN): 
Reality: an image contains cat. 
classifier predicts: no cat.


Can anyone gives a concrete example of TP、TN、FP、FN like above, in the context of natural language processing?
Imagine a hot news classifier.

True Positive (TP): 
Reality: a piece of hot news. 
classifier predicts: hot.

True Negative (TN): 
Reality: not a piece of hot news. 
classifier predicts: not hot.

False Positive (FP): 
Reality: not a piece of hot news.
classifier predicts: hot.

False Negative (FN): 
Reality: a piece of hot news. 
classifier predicts: not hot.