core feature is its polarity classifier, which implements a multiclass Support Vector Machine (SVM) algorithm [@hall_weka_2009] combining the information extracted from polarity lexicons with linguistic features obtained from the NLP pre-processing step. Main features include polarity values from general and domain specific polarity lexicons, lemma and POS tag ngrams and positivity and negativity counts based on polarity lexicons. Features representing other linguistic phenomena such as treatment of negation, locutions or punctuation marks are also included. Finally, there are some social media specific features, such as the proportion of capitalized symbols (which often is used to increase the intensity of the message) or emoticon information.

EliXa currently provides ready to use polarity classification models, although one of its strengths is that new models can easily be trained if training data is available for a new domain.

User profiling {#isv2018:da:profiling}
--------------

Talaia is also capable of providing deeper analysis of the data, by means of user profiling. Specifically, geolocation, gender detection and user community identification are implemented.

Opinions gathered are geolocated. Geolocation may be done using two different approaches: (i) building a census of twitter users on a region or (ii) trying to geolocate the origin of the users detected. Approach (i) 