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Image sentiment analysis is an important topic nowadays. It is possible
to use it to classify an image at sentiment level, as negative, neutral or
positive. However, to classify an image at this level is a hard challenge
because its semantic meaning can represent many scenarios. In this paper,
we present an analysis of several image classification methods that we
evaluate to improve the state of the art in a large tweet data set.