AVOIDING SENTIMENT MODEL OVERFITTING IN A MACHINE LANGUAGE MODEL

Provided are techniques for avoiding sentiment model overfitting in a machine language model. A current list of keywords in a current sentiment model can be updated to create a proposed list of keywords in a proposed sentiment model. Machine-generated sentiment results, based on the proposed sentiment model, are presented to identify model overfitting, without revising the current set of keywords. The proposed set of keywords can be edited, and when overfitting is not present, the current list of keywords is replaced by the proposed list of keywords.