  Social media are becoming an increasingly important source of information
about the public mood regarding issues such as elections, Brexit, stock market,
etc. In this paper we focus on sentiment classification of Twitter data.
Construction of sentiment classifiers is a standard text mining task, but here
we address the question of how to properly evaluate them as there is no settled
way to do so. Sentiment classes are ordered and unbalanced, and Twitter
produces a stream of time-ordered data. The problem we address concerns the
procedures used to obtain reliable estimates of performance measures, and
whether the temporal ordering of the training and test data matters. We
collected a large set of 1.5 million tweets in 13 European languages. We
created 138 sentiment models and out-of-sample datasets, which are used as a
gold standard for evaluations. The corresponding 138 in-sample datasets are
used to empirically compare six different estimation procedures: three variants
of cross-validation, and three variants of sequential validation (where test
set always follows the training set). We find no significant difference between
the best cross-validation and sequential validation. However, we observe that
all cross-validation variants tend to overestimate the performance, while the
sequential methods tend to underestimate it. Standard cross-validation with
random selection of examples is significantly worse than the blocked
cross-validation, and should not be used to evaluate classifiers in
time-ordered data scenarios.
