For each document in the dataset, we break up the document into sentences. Each sentence is then assigned an affect probability for each of the nine classes. These sentence probabilities are averaged at the document level to create document-level scores which are presented in the article drawer. The document-level probabilities are then averaged for all of the documents in a specific publication date range to track affect over time.

For the example of the September 11 terrorist attack, we can see that emotion changes drastically on September 12th following the terrorist attack. The proportion of negative emotions, namely Sadness and Fear, increases by 50-100%. Both of these emotions remain at higher levels for the remainder of September—How long does it take for expressed Sadness and Fear to return to pre-Sept. 11 levels? Also interestingly, we can see that that other negative emotions, such as Anger and Disgust, do not increase following the terrorist attack.