situations [@griffith2016analysis; @shaikh2017tweet; @ozturk2018sentiment]. The growing number of SA related shared tasks (e.g., SemEval Aspect based SA and Twitter SA shared tasks) or the commercial platforms for reputation management (see section \[isv2018:soa:indust\]) are proof of the interest from both academic and market worlds.

The particularities of its language make it hard to analyze tweets. User mentions, hashtags, the growing presence of emojis, ungrammatical sentences, vocabulary variations and other phenomena pose a great challenge for traditional NLP tools [@foster_hardtoparse2011; @liu_recognizing_2011]. @brody_cool2011 deal with the word lengthening phenomenon, which is especially important for sentiment analysis because it usually expresses emphasis of the message. Hashtag decomposition (e.g.,*\#GameOfThrones = ‘Game Of Thrones’*) [@BrunRoux_F14-2015; @Belainine_W16-3915] or matching Out Of Vocabulary (OOV) forms and acronyms to their standard vocabulary forms (e.g., [*‘imo = in my opinion’*]{}) [@han_lexical_2011; @liu_broad-coverage_2012; @alegria2014tweetnorm] are other addressed issues. International benchmarking initiatives such as the TweetNorm shared task[@tweetnorm_jlre_2015] or the WNUT[^2] workshop series are proof of the interest to solve this task.

Once texts are normalized, sentiment analysis can be performed. Several ruled-based systems to polarity classification have been proposed [@hu_mining_2004; @thelwall2017heart; @taboada2011lexicon]. Nevertheless, we will focus on Machine Learning (ML) based approaches which are the most widespread. Support Vector Machines (SVM) 