$\rightarrow$ ‘a very long day’). Also Out Of Vocabulary (OOV) term normalization is addressed by means of language specific frequency lists based on Twitter corpora.

Furthermore, EliXa’s normalization component also includes various specific functionalities related to SA:

-   Emoticons are normalized into a 7 sentiment scale: *smiley, crying, shock, mute, angry, kiss, sadness*.

-   Expressions that are meaningful for detecting SA such as interjections and onomatopoeia are marked.

Those normalized terms must be included in the polarity lexicons in order to have a greater impact in the sentiment analysis classification. Table \[isv2018:da:pre:rsrc-table\] presents the resources provided for normalization according to their use. Word form dictionaries are composed of word forms extracted from corpora. When applying microtext normalization, candidates are compared to forms in the dict in order to discard noisy candidates. For example, if we were to normalize “happppy”, we would know the that the correct normalization is “happy” by looking at theses dictionaries.

OOV dictionaries are composed of “OOV - standard form” pairs. These resources are valuable to normalize slang and commonly used abbreviations. In order to produce such dictionaries word form frequency lists were generated from Twitter corpora, and after pruning standard dictionary forms, the most frequent $n$ 