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The rise of digital communication systems provides an almost infinite source of information that can be useful to feed classification algorithms, so it makes use of an already categorized collection of opinions of the social network Twitter for the formation and generation of a model of classification of short texts; which aims to categorize the emotional tone found in an author's Spanish-language digital text. In addition, linguistic, lexicographic and opinion mining computational tools are used to implement a series of methods that allow to automatically finding coincidences or orientations that allow determining the polarity of sentences and categorize them as positive, negative or neutral considering their lemmas. The results obtained from the analysis of emotions and polarity of this project, on the test phrases allow to observe a direct relationship between the categorized emotional tone and it is positive, negative or neutral classification, which allows to provide additional information to know the intention that the author had when he created the sentence. Determining these characteristics can be useful as a consistent information objective that can be leveraged by sectors where the prevalence of a product or service depends on user opinion, product rating or turns with satisfaction metrics. [ABSTRACT FROM AUTHOR]

Language is in constant evolution – this theory has been demonstrated most aptly and comprehensively by Marshall McLuhan. Specialisation in the different areas of knowledge, especially technology, has contributed to this process. Technological advances and the development of so-called intelligent devices allow interaction through voice interfaces, text, or gesture and in its most advanced forms by means of the incorporation of artificial intelligence-generated linguistic communications in human-machine interfaces. In recent years, the ways of communication or watching news have changed, now we do it by means of the internet and through different options of the social networks. We interact with people and react to their communications by means of divergent ways of language formation. It is increasingly common to express opinions through social networks and the internet. So much so that now we know that it is possible to analyse a person’s sentiment from his or her communications of opinion issued in social networks? The question is, can we determine, for example, whether the opinion has a positive or negative emotive charge only by analysing the written or inscribed texts of such formats of communication? This paper presents a brief description of how technological evolution has created an x-factor of language, that is expressed, appropriated and re-used in machine learning modules, artificial intelligence, and automatic sentiment analysis. [ABSTRACT FROM AUTHOR]

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