  This paper discusses the fourth year of the ``Sentiment Analysis in Twitter
Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a
significant departure from previous editions. The first two subtasks are reruns
from prior years and ask to predict the overall sentiment, and the sentiment
towards a topic in a tweet. The three new subtasks focus on two variants of the
basic ``sentiment classification in Twitter'' task. The first variant adopts a
five-point scale, which confers an ordinal character to the classification
task. The second variant focuses on the correct estimation of the prevalence of
each class of interest, a task which has been called quantification in the
supervised learning literature. The task continues to be very popular,
attracting a total of 43 teams.
