## 2 Background Information
### Opinion Mining  
Opinion mining (OM) is a natural language processing (NLP) technique that creates predictive models capable of inferring the subjective opinion towards a specific topic. Opinion mining can handle different degrees of specificity. In their most simpler approach, it can classify documents as a whole (document-level classification), by predicting an unique sentiment in the whole document. Document-level approaches are effective when the goal is to discover a general predisposition. Besides, when the documents to be classify are large, document-level is more reliable than other approaches because it provides better word co-occurrence for finding discriminating features. Nevertheless, as users can like or dislike certain aspects of the same topic, or even express contradictory ideas, it is possible to split the documents into: (1) sentences (sentence-level classification) or (2) aspects (aspect-level classification). An an aspect level classification consists of the representation of sentiments as triplets {user, feature, sentiment}. In this sense, ontologies have been proved to be an effective method to extract the aspects for an specific domain [11; 12; 13; 14; 15]. The polarity of an utterance can be obtained by two approaches: (1) semantic-orientation, and (2) machine-learning. Semantic-orientation consists of the identification of specific linguistic units that are associated to sentiments. In this sense, it is needed the use of lexicons such as SentiWordNet [17], that contains words with scores related to positive and negative sentiments. A machine-learning approach, on the other hand, consists of the training of a machine-learning classifier from a labelled corpus. A machine-learning approach requires to encode texts as vectors. Bag of words (BoW) is a statistical-model that measures the frequency of each word in the corpus. BoW model is widely used as a baseline model for some NLP tasks; however, it presents some drawbacks: (1) it considers words without context, so it does not take into account linguistic phenomenons such as the figurative language, the presence of typos, or grammatical errors; (2) it is not truly language-independent. Language independence means that, once an algorithm is developed, it can trivially be extended to another language as long as we have available a large amount of training data. But in the BoW model, this is only true for non-agglutinative and western languages, such as Spanish, English, Italian or Portuguese, where words usually have a single inflectional morpheme to denote multiple grammatical, syntactic, or semantic features. In agglutinative languages, on the other hand, complex words are formed by stringing together morphemes without changing them in spelling or phonetics which hinders the frequency of the word-grams; and (3) it suffers from the curse of dimensionality, because it tends to create over-fitted models due to the vocabulary size. This problem is aggravated in agglutinative languages because it produces a large number of word forms that can be obtained from a single root. In order to solve these drawbacks some researchers have proposed different alternatives. For example, some researchers argue that the use combinations of joint words minimize the absence of context [18]. In this sense, bigrams and trigrams are capable of extract concepts expressed with compound words, such as the bigram New York instead of the unigrams New and York. On the other hand, n-grams generates even large vectors that aggravate the high dimensionality problem and may introduce redundant features. The usage of sequences of characters (char-grams), instead words, has also been proposed [19]. Char-grams presents some advantages over the word-grams model: First, char-grams capture (1) lexical information, such as the use of punctuation symbols, and (2) morphological information, such as prefixes or suffixes. Second, char-grams are robust against grammatical errors, because a misspelled word and its correct version should share common char-grams.