Emotion Recognition System Project of English Newspapers to Regional E-Business Adaptation

Autor(en): Voloshyn, Serhii
Markiv, Oksana
Vysotska, Victoria
Dyyak, Ivan
Chyrun, Lyubomyr
Panasyuk, Valentyna
Stichwörter: classification; Classification (of information); dataset; Embeddings; emotion recognition; English; Intellectual systems; Machine learning; Machine-learning; neural network; Neural-networks; newspaper; Newsprint; NLP; Recognition systems; Speech recognition; Word embedding; words embedding
Erscheinungsdatum: 2022
Herausgeber: Institute of Electrical and Electronics Engineers Inc.
Journal: International Scientific and Technical Conference on Computer Sciences and Information Technologies
Volumen: 2022-November
Startseite: 392 – 397
Zusammenfassung: 
Main goal of the work is emotion recognition and sentiment analysis of quotes from English newspapers. Key of this problem is task solution of text emotional analysis, that is, feelings analysis of author who expresses own opinion. Emotional or sentimental text analysis is primarily the classification algorithm aimed at finding certain point and its location and information highlighting particular interest in the process. In this work, the problem of multi-class or binary classification is solved using machine learning. Publication describes general information about human language processing, brief description of analogues, use of machine learning to analyse emotions in text. Article describes the sequence of intellectual system project realization actions and the used tools for emotion recognition. Then, the statistics of tasks performed by neural network with various functions of activators are described. In the publication control example of demo version of the intellectual system project was made, and the main usage scenario was depicted. At the end, the results of the study have been summarized. © 2022 IEEE.
Beschreibung: 
Cited by: 2; Conference name: 17th IEEE International Conference on Computer Science and Information Technologies, CSIT 2022; Conference date: 10 November 2022 through 12 November 2022; Conference code: 185883
ISBN: 9798350334319
ISSN: 2766-3655
DOI: 10.1109/CSIT56902.2022.10000527
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146347276&doi=10.1109%2fCSIT56902.2022.10000527&partnerID=40&md5=77d24d63802419b7e8abe87ac37f8f05

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