2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 1 , PP: 177-185, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Janneth Ximena Iglesias Quintana 1 * , Monica Alexandra Salame Ortiz 2 , Alipio Absalon Cadena Posso 3 , Joffre R. Paladines Rodriguez 4 , Bekbayeva Feruza Baxtiyerovna 5
Doi: https://doi.org/10.54216/JISIoT.120114
The present work tackles an urgent problem in the area of data analytics that is the shifting of sentiment against language in regards to human cognition. Although the science of data mining and machine learning has done much to address the problem of these tools, their scope is still limited regarding the management of human language which has inherent uncertainty and ambiguity. This research seeks to address this gap by illustrating how to apply a machine learning tool in a way that embraces the so-called uncertainty neutrality using the orange data analysis tool for analysis of visualized data. It is also important to note in the research that the combination of neutral and intelligent analysis with using applications such as orange increases the efficiency of sentiment classification and expands the theoretical scope of sentiment data analysis. Their findings underscore that this perspective seeks to illuminate details which other methods tend to ignore and hence offer a much more nuanced understanding of human cognition. Practically, this research presents an efficient paradigm as the new framework can be employed in market intelligence, evaluation of public policy and intelligent interface design, among others. As a result, this research does not only contribute to the body of knowledge within the profession of data science but also explores new dimensions in understanding human cognition.
Neutrosophic analysis , Human perceptions , Sentiment analysis , Orange data analysis , Data mining , uncertainty , Ambiguity , Human language , Data visualization , Sentiment classification
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