2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 213-323, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ned Vıto Quevedo Arnaız 1 * , Genaro Vınıcıo Jordan Naranjo 2 , Diego Xavier Chamorro Valencia 3 , Joffre Joffre Paladines Rodríguez 4 , Anna Mixaylovna Aripova 5
Doi: https://doi.org/10.54216/FPA.160214
Sentiment analysis has recently become popular in social, political and health related fields, but it has a common limitation of capturing the subjectivity involved in multiple human expressions. In this study, we tackle this concern by presenting a model that is constructed using neutrosophic logic which can incorporate indeterminacy in the evaluation of perceptions. Although some answers may be provided by the traditional methods, they fail to contain the uncertainties and contradictions which are characteristic of natural language, making them difficult to implement in complicated situations. In this methodological gap, the neutrosophic model is presented as a tool capable of overcoming these limitations by explicitly treating uncertainty and balancing definite, indeterminate, and contradictory elements. The integration of machine learning algorithms with neutrosophic techniques helps classify and visualize sentiments embedded in big volume of text data. The findings suggest that this methodology not only enhances the precision in the identification of emotional subtleties but also provides a hybrid platform for integrating imprecise information. His credits are based on the development of a theoretical model which advances the field of sentiment analysis and the development of real-life applications in customer services for example, political analytics and strategic decision making. This methodological advance demonstrates that incorporating neutrosophic logic into sentiment data analysis opens up new possibilities for understanding and modeling the complexities of human perceptions.
Neutrosophic model , Sentiment analysis , Human perception , Subjectivity , Ambiguity , Uncertainty , Contradictions , Neutrosophic logic , Natural language , Machine learning
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