  Sentiment quantification is the task of training, by means of supervised
learning, estimators of the relative frequency (also called ``prevalence'') of
sentiment-related classes (such as \textsf{Positive}, \textsf{Neutral},
\textsf{Negative}) in a sample of unlabelled texts. This task is especially
important when these texts are tweets, since the final goal of most sentiment
classification efforts carried out on Twitter data is actually quantification
(and not the classification of individual tweets). It is well-known that
solving quantification by means of ``classify and count'' (i.e., by classifying
all unlabelled items by means of a standard classifier and counting the items
that have been assigned to a given class) is less than optimal in terms of
accuracy, and that more accurate quantification methods exist. Gao and
Sebastiani (2016) carried out a systematic comparison of quantification methods
on the task of tweet sentiment quantification. In hindsight, we observe that
the experimental protocol followed in that work was weak, and that the
reliability of the conclusions that were drawn from the results is thus
questionable. We now re-evaluate those quantification methods (plus a few more
modern ones) on exactly the same same datasets, this time following a now
consolidated and much more robust experimental protocol (which also involves
simulating the presence, in the test data, of class prevalence values very
different from those of the training set). This experimental protocol (even
without counting the newly added methods) involves a number of experiments
5,775 times larger than that of the original study. The results of our
experiments are dramatically different from those obtained by Gao and
Sebastiani, and they provide a different, much more solid understanding of the
relative strengths and weaknesses of different sentiment quantification
methods.
