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Language & Knowledge Engineering is essential for the successfully development of artificial intelligence. The technologies proposed in international forums are meant to improve all areas of our daily life whether it is related to production industries, social communities, government, education, or something else. We consider very important to reveal the recent advances Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering because they are the base for the society of tomorrow. Thus, the aim of this special issue of Journal of Intelligent and Fuzzy Systems is to present a collection of papers that cover recent research results on the two wide topics: language and knowledge engineering. Even if the special issue is structured into these two general topics, we have covered specific themes such as the following ones: Natural Language Processing, Knowledge engineering, Pattern recognition, Artificial Intelligence and Language, Information Processing, Machine Learning Applied to Text Processing, Image and Text Classification, Multimodal data analysis, sentiment analysis, etc. [ABSTRACT FROM AUTHOR]

The aim of this article is to contextualize and describe the gathering and annotation of a conventual Hispanic and Novo Hispanic texts corpus for emotions identification. Such corpus will be the dataset for an emotions identification model based on machine learning ∖ deep learning techniques. Furthermore, this document describes several exploratory experiments carried out on the corpus. Within these experiments, it is described how the corpus is also used to obtain a lexicon mapped to polarities and emotions, and how some of the documents are hand-labeled by experts for the evaluation of the Machine Learning ∖ Deep learning -based emotion classification model. Finally, the future uses and experiments with said corpus are described. [ABSTRACT FROM AUTHOR]

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]

Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting and analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifiers succeed over individual base classifiers. [ABSTRACT FROM AUTHOR]