similar three step approach with a more complex network topology obtaining state of the art results for various tasks, including sentiment analysis.

A common problem of supervised approaches, specially of DL, is the need of large amounts of labeled data for training. The common practice in the literature is to gather weakly supervised datasets following the emoticon heuristic[^3] [@go_twitter_2009]. This is feasible for major languages, but it is a very difficult (if possible at all) and time costly task for non major languages such as Basque.

Our system is closest to @barbosa_robust_2010 and @kouloumpis_twitter_2011 because it combines polarity lexicons with machine learning for labelling sentiment of tweets. This strategy has proven to be a successful approach in previous shared tasks [@saralegi_tass_2012; @mohammad_semeval_2013].

Industrial Solutions {#isv2018:soa:indust}
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We can find various commercial solutions in the market. We are particularly interested in systems that provide an integral solution of the monitoring process, leaving out tools that only approach specific phases of the surveillance process, or solutions that offer bare NLP processing chains which require further development to achieve a working social media monitor. Table \[isv2018:soa:indust:comptable\] in Annex offers a detailed comparative of the tools analyzed. We focus our analysis on the sources 