and Logistic Regression algorithms have been the very popular for polarity classification as various international shared tasks [@roman_tass2014; @semeval-2014_4; @semeval-2014_9] show. Typical features of those systems include sentiment word/lemma ngram features, POS tags [@barbosa_robust_2010], Sentiment Lexicons [@kouloumpis_twitter_2011], emoticons [@oconnor_tweets_2010], discourse information [@somasundaran_supervised_2009] or, more recently, word embeddings [@mikolov2013distributed] and clusters [@elixa-semeval2015].

From 2015 onwards, the academic world has shifted to Deep Learning (DL) approaches, as @Semeval2016-Twitter-task4 confirm. Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNN)[@DaiLe2015NIPS; @johnson2016supervised] and Convolutional Neural Networks (CNN) are the preferred choices. @severyn2015 use a single layer CNN, first to construct word embeddings and then to train the classifier. @Deriu2017 propose a two phase training method: first they train a neural network with large amounts of weakly supervised data collected from Twitter. The network is initialized with word embeddings learned by means of word2vec [@mikolov2013distributed] from very large corpora collected from twitter. Second, the weights learned in the first step are transferred to a second neural network trained over the actual annotated data, to learn the final classifier. A two convolutional layer CNN is used for both training phases. A very similar approach is followed by @Cliche2017 which achieves top results in SemEval [@Semeval2017-twitter-task4]. @ulmfit2018 follow a 