Abstract
This paper presents the system we developed for Task 11 of SemEval 2015. Our system had two stages: The first one was based on deep autoencoders for extracting features to compactly represent tweets. The next stage consisted of a classifier or a regression function for estimating the polarity value assigned to a given tweet. We tested several techniques in order to choose the ones with the highest accuracy.
Finally, three regression techniques revealed as the best ones for assigning the polarity value to tweets. We presented six runs corresponding to three regression different techniques in combination with two variants of the autoencoder, one with input as bags of words
and another with input as bags of character 3-grams.
