for that. First, it allows us implement the aforementioned “language specific keyword” feature. Second, having the language identified in the first place gives us flexibility for applying the subsequent NLP tools. At the moment language identification is implemented using the library Optimaize[^16], combined with source specific optimizations (social media vs. feeds).

Data Analysis {#isv2018:da}
=============

The data analysis is mainly performed by EliXa [@elixa-semeval2015] which integrates the following processes, each of them further detailed in the next sections.

EliXa[^17] is a supervised Sentiment Analysis system. It was developed as a modular platform which allows to easily conduct experiments by replacing the modules or adding new features. It was first tested in the ABSA 2015 shared task at SemEval workshop [@semeval-2015-absa]. EliXa currently offers resources and models for 4 languages: Basque, Spanish, English and French. Its implementation is easily adaptable to other languages, requiring a polarity lexicon and/or a training dataset for each new language.

Normalization {#isv2018:da:pre}
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To address the particular characteristics of tweets, EliXa integrates a microtext normalization module which is applied to social media messages, based on @saralegi_tweetnorm_2013. The normalizer is based on heuristic rules, such as standardizing URLs, normalizing character repetitions or dividing long words (e.g. \#AVeryLongDay 