  In Ordinal Classification tasks, items have to be assigned to classes that
have a relative ordering, such as positive, neutral, negative in sentiment
analysis. Remarkably, the most popular evaluation metrics for ordinal
classification tasks either ignore relevant information (for instance,
precision/recall on each of the classes ignores their relative ordering) or
assume additional information (for instance, Mean Average Error assumes
absolute distances between classes). In this paper we propose a new metric for
Ordinal Classification, Closeness Evaluation Measure, that is rooted on
Measurement Theory and Information Theory. Our theoretical analysis and
experimental results over both synthetic data and data from NLP shared tasks
indicate that the proposed metric captures quality aspects from different
traditional tasks simultaneously. In addition, it generalizes some popular
classification (nominal scale) and error minimization (interval scale) metrics,
depending on the measurement scale in which it is instantiated.
