首页|New Machine Learning Findings from Technical University of Kosice Described (Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak)
New Machine Learning Findings from Technical University of Kosice Described (Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting from Technical University of Kosice by NewsRx journalists, research stated, “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.” Financial supporters for this research include Ministry of Education, Science, Research And Sport of The Slovak Republic; Slovak Research And Development Agency; Faculty of Electrical Engineering And Informatics, Tu Kosice. The news reporters obtained a quote from the research from Technical University of Kosice: “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.”
Technical University of KosiceCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Mdpi
