Automatic detection of emotional stress in speech has recently drawn increasing attention, mainly in the fields of natural and spoken language processing. Stress can be automatically detected from speech-derived parameters, such as fundamental frequency F0, voice intensity, jitter, shimmer, harmonic-to-noise ratio, mel-frequency cepstral coefficients, etc. In this paper, we focus on the analysis of selected voice characteristics in available datasets of emotional speech and speech under stress in the English and Slovak languages. We also proposed a stress level detection approach, which uses the fusion of the Teager energy operator (TEO) with mel-frequency cepstral coefficients (MFCCs) and classification based on decision trees and ensemble learning method of bagging decision trees. Preliminary results of 64% classification accuracy are comparable to other studies in this field of research.

In the article, we describe recent trends in the detection of hate speech and offensive language on social media. We accord from the latest studies and scientific contributions. The article describes current trends and the most used methods in connection with the detection of hate speech and offensive language. At the same time, we focus on the importance of emoticons, hashtags, and swearing in the field of social networks. We point out the topicality of the selected topic, describe the next direction of our work, and suggest possible solutions to current problems in this field of research.

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%.

In this article, we focus on the comparison of classifiers on datasets in the Slovak language. In order to compare the effectiveness of the classifiers, we used two groups of data: the first group was obtained from the Internet and translated into the Slovak language, while the second group was originally in the Slovak language. Our goal was to find out how the translation of datasets affects the performance of classifiers and to identify the most suitable models for the Slovak language.

In the article, we describe comparison of different architectures for machine translation of the Slovak language using the Fairseq toolkit. The article describes current trends and the most used architectures (e.g. convolutional neural network, Long short-term memory, recurrent neural network and Transformer) in connection with machine translation. We also focus on the issue of parallel corpus and its use in connection with machine translation. We have created a short overview of the issue, we described our experimental results, and suggested possible solutions to problems in machine translation field for Slovak language.

The paper introduces a very current topic in the field of natural language processing oriented to the automatic detection of hate speech and offensive language performed in the Slovak language. In this work, we describe the creation and processing database of short texts composed of posts and comments written in Slovak and published on social media. The proposed approach is based on sentiment analysis and implementing a tool for detecting hate speech using a convolutional neural network with elements of a recursive neural network, applied to a created database of comments. We achieved 61.32% detection accuracy only on a small set of training data balanced in the number of positive, neutral, and negative sentiments.