This article compares EVE-NG, a network emulation software, to other commonly used network simulation tools such as Cisco Packet Tracer and GNS3. EVE-NG’s features, benefits, and drawbacks are assessed in terms of system requirements, support platforms, web interface, console access, topology management, and advanced lab functionalities. EVE-NG is tested by the students in the real-world application, and feedback from a group of students is gathered regarding their satisfaction and suggestions for improvement. The results of this study show that EVE-NG provides a robust and versatile tool for simulating complex network scenarios, despite compatibility, reliability, and performance issues. It is concluded that EVE-NG has the potential to improve student learning outcomes in computer networks; however, ongoing development and refinement are necessary to meet user expectations and needs.

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.

We propose a Slovak language model for the spaCy library in Python. These models are easy-to-use for basic natural language processing tasks in a single package. The package contains several components for basic preprocessing tasks, such as tokenization, sentence boundary detection, syntactic parsing, lemmatization, named entity recognition, morphology analysis, and word vectors. It is based on the state-of-the-art monolingual SlovakBERT model. Named entity recognition is trained on a separate, publicly available WikiAnn database. The other statistical classifiers use a Slovak Dependency Treebank corpus. Morphological tags are compatible with the conventions of the Slovak National Corpus. The part of speech tags use conventions of the Universal Dependencies framework. We trained a separate word vector model on a web-based corpus. The training uses fastText with Floret modification. We present a series of experiments that confirm that the model performs similarly to other languages for all tasks. Training scripts and data are publicly available.

The rise of social media has led to an increase in toxic language, hate speech, and offensive content. While extensive research exists for widely spoken languages like English, Slovak remains underrepresented due to the lack of high-quality datasets. This gap limits the development of effective models for toxicity detection and sentiment analysis in Slovak. To address this, we introduce three new annotated Slovak datasets focused on toxic language, offensive language, hate speech detection, and sentiment analysis. These native datasets provide a more reliable foundation for automated moderation compared to machine-translated alternatives. Our research also highlights the real-world impact of online toxicity, including social polarization and psychological distress, emphasizing the need for proactive detection systems on social media platforms. This paper reviews existing Slovak datasets, presents our newly developed resources, and provides a comparative analysis. Finally, we outline key contributions and suggest future directions for improving toxic language detection in Slovak.