Machine learning model using keywords for binary decision
The most obvious binary classification task coming from text data is sentiment analysis. Kaggle is plenty of datasets such as this one on movie reviews.

Sentiment classifiers can be either very simple, like logistic regressions on naive Bayes classifiers, or very complex such as the ones based on RNNs and/or part-of-speech tagging models.

If you are interested in news specifically, similar models can be used to estimate news polarization.