Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Roy, Alphin; Roy, Apash; Shrivallabha, S. Title A name given to the resource Mitigating Subjectivity and Annotation Inconsistencies in Sentiment Analysis via an SVM-RoBERTa Ensemble Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/DECoN67170.2025.11447642" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/DECoN67170.2025.11447642</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037371064?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037371064?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Roy A., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Roy A., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Shrivallabha S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India Description An account of the resource This research addresses a main limitation in the Natural Language Processing that is the impact of subjectivity and annotation inconsistencies on the accuracy of the sentiment classification. We did a systematic comparison of two fundamentally different architectures. A traditional feature based Support Vector Machine and a deep contextual fine tuned RoBERTa transformer using a challenging, noisy, real-world Twitter dataset. This corpus retains ambiguity and sarcasm on purpose and serve the crucible for testing model robustness. We developed a soft voting ensemble method that combines the probability scores from both models to obtain the best predictive capabilities. The results showed a clear technological hierarchy. The RoBERTa model with its deep semantic grasp outperformed the SVM by a substantial margin achieving 90% accuracy against 83.5% accuracy. But the hybrid ensemble model attained the highest overall accuracy of 91.35% and showed better reliability across all the sentiment classes. These findings shows that a hybrid approach fusing a transformer's nuanced understanding with the stabilization provided by ensemble learning is the most effective and robust method for mitigating data imperfections in modern sentiment analysis. 2025 IEEE. Subject The topic of the resource Annotation Inconsistency; Ensemble Learning; RoBERTa; Sentiment Analysis; Support Vector Machine Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159442-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online