A massive amount of textual data, such as e-mails, reports, articles and posts in social networks or blogs, has been generated and stored on a daily basis. The manual processing, organization and management of this huge amount of texts require a considerable human effort and sometimes these tasks are impossible to carry out in practice. Besides, the manual extraction of knowledge embedded in textual data is also unfeasible due to the large amount of texts. Thus, computational techniques which require little human intervention and allow the organization, management and knowledge extraction from large amounts of texts have gained attention in the last years and have been applied in academia, companies and organizations. The tasks mentioned above can be carried out through text automatic classification, in which labels (identifiers of predefined categories) are assigned to texts or portions of texts. A viable way to perform text automatic classification is through machine learning algorithms, which are able to "learn", generalize or extract patterns from classes of text collections based on the content and labels of the texts. There are three types of machine learning algorithms for automatic classification: (i) inductive supervised, in which only labeled documents are considered to induce a classification model and this model are used to classify new documents; (ii) transductive semi-supervised, in which all known unlabeled documents are classified based on some labeled documents; and (iii) inductive semi-supervised, in which labeled and unlabeled documents are considered to induce a classification model in order to classify new documents. Regardless of the learning algorithm type, the texts of a collection must be represented in a structured format to be interpreted by the algorithms. Usually, the texts are represented in a vector space model, in which each text is represented by a vector and each dimension of the vector corresponds to a term or feature of the text collection. Algorithms based on vector space model consider that texts, terms or features are independent and this assumption can degrade the classification performance. Networks can be used as an alternative to vector space model representations. Networks allow the representations of relations among the entities of a text collection, such as documents and terms. This type of representation allows the extraction patterns which are not extracted by algorithms based on vector-space model. Moreover, text collections can be represented by networks composed of different types of entities and relations, which provide the extraction of different patterns from the texts. However, there are some challenges to be solved in order to allow the combination of machine learning algorithms and network-based representations to perform text automatic classification in an efficient way. The main challenges addressed in this doctoral project are (i) the development of network-based representations efficiently generated which also allows an efficient learning; (ii) the deve...