  Sentiment Classification is a fundamental task in the field of Natural
Language Processing, and has very important academic and commercial
applications. It aims to automatically predict the degree of sentiment present
in a text that contains opinions and subjectivity at some level, like product
and movie reviews, or tweets. This can be really difficult to accomplish, in
part, because different domains of text contains different words and
expressions. In addition, this difficulty increases when text is written in a
non-English language due to the lack of databases and resources. As a
consequence, several cross-domain and cross-language techniques are often
applied to this task in order to improve the results. In this work we perform a
study on the ability of a classification system trained with a large database
of product reviews to generalize to different Spanish domains. Reviews were
collected from the MercadoLibre website from seven Latin American countries,
allowing the creation of a large and balanced dataset. Results suggest that
generalization across domains is feasible though very challenging when trained
with these product reviews, and can be improved by pre-training and fine-tuning
the classification model.
