Solving sentiment uncertainty using newly proposed sentiment similarity measure for single-valued neutrosophic sets

Divya Arora, Devendra K. Tayal () and Sumit K. Yadav
Additional contact information
Divya Arora: Indira Gandhi Delhi Technical University for Women
Devendra K. Tayal: Indira Gandhi Delhi Technical University for Women
Sumit K. Yadav: Income Tax Department

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 9, No 17, 3209-3234

Abstract: Abstract The accurate detection of sentiment polarity in social media content is a persistent challenge, particularly in the absence of large labeled datasets and inherent linguistic ambiguity. Existing machine learning and fuzzy set-based approaches often struggle to capture the indeterminate and complex nature of real-world sentiment. In this work, we introduce a novel similarity measure for Single-Valued Neutrosophic Sets in a three-dimensional sentiment space, enabling a more expressive and robust representation of positive, negative, and indeterminate sentiment components Building upon this foundation, we propose SentiSim, an unsupervised and domain-neutral sentiment analysis framework that leverages the Grey Wolf Optimizer to identify optimal sentiment prototypes and enhance classification performance. SentiSim processes textual data using lexicon-based mapping to neutrosophic vectors, optimizes sentiment cluster centers via metaheuristic search, and classifies sentiment by maximizing similarity to these centers. Extensive experiments on benchmark datasets including SemEval 2017, Sentiment140, and Apple X demonstrate that SentiSim consistently outperforms state-of-the-art methods, with statistical analyses confirming the significance and robustness of its improvements. The proposed framework enhances the interpretability and reliability of sentiment analysis under uncertainty and indeterminacy.

Keywords: Neutrosophic sets; Similarity measure; Sentiment similarity; Grey wolf optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02853-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:9:d:10.1007_s13198-025-02853-x

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02853-x

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().