Modeling Non-Standard Text Classification Tasks

Text classification deals with discovering knowledge in texts and is used for extracting, filtering, or retrieving information in streams and collections. The discovery of knowledge is operationalized by modeling text classification tasks, which is mainly a human-driven engineering process. The outcome of this process, a text classification model, is used to inductively learn a text classification solution from a priori classified examples. The building blocks of modeling text classification tasks cover four aspects: (1) the way examples are represented, (2) the way examples are selected, (3) the way classifiers learn from examples, and (4) the way models are selected. This thesis proposes methods that improve the prediction quality of text classification solutions for unseen examples, especially for non-standard tasks where standard models do not fit. The original contributions are related to the aforementioned building blocks: (1) Several topic-orthogonal text representations are studied in the context of non-standard tasks and a new representation, namely co-stems, is introduced. (2) A new active learning strategy that goes beyond standard sampling is examined. (3) A new one-class ensemble for improving the effectiveness of one-class classification is proposed. (4) A new model selection framework to cope with subclass distribution shifts that occur in dynamic environments is introduced

Influence of social networks on the analysis of sentiment applied to the political situation in Ecuador

Knowing the opinion of a sector of the population can be as important to launch a product in marketing, as to know the opinion of voters, in politics. In Ecuador, the social network Twitter has become one of the main means of direct interaction between political figures and the population. So a study that reflects feelings in Spanish by idioms of each region, gives us a great opportunity to study the relationship between the level of acceptance on Twitter of a candidate and the election results. The contribution of this article is the analysis of feelings (SA) using a tool for NLP adapted to the variation of Spanish used in Ecuador, taking advantage of the fact that most of the literature has focused on the English language, while adaptations for languages, like Spanish, they are minimal and are still in process due to the complexity inherent in the language

Influence of social networks on the analysis of sentiment applied to the political situation in Ecuador

Knowing the opinion of a sector of the population can be as important to launch a product in marketing, as to know the opinion of voters, in politics. In Ecuador, the social network Twitter has become one of the main means of direct interaction between political figures and the population. So a study that reflects feelings in Spanish by idioms of each region, gives us a great opportunity to study the relationship between the level of acceptance on Twitter of a candidate and the election results. The contribution of this article is the analysis of feelings (SA) using a tool for NLP adapted to the variation of Spanish used in Ecuador, taking advantage of the fact that most of the literature has focused on the English language, while adaptations for languages, like Spanish, they are minimal and are still in process due to the complexity inherent in the language

Інтелектуальні системи та прикладна лінгвістика

В матеріалах розглядаються проблеми та перспективи розвитку інтелектуальних комп’ютерних систем та різних галузей прикладної лінгвістики, а саме корпусної лінгвістики, комп'ютерної лексикографії, машинного перекладу, лінгвістики Інтернету; питання використання інформаційних технологій в лінгвістиці, з метою дослідження та обробки мови

Modeling Non-Standard Text Classification Tasks Faculty of Media

Dr. rer. nat