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 Mukku, Lalasa; Burri, Vikas Reddy; Lamani, Manjunath Ramanna Title A name given to the resource Mul-Sensis: Multilingual Sentiment Analysis Framework for Emotion Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;454 SIST;pp.237-247 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-07837-7_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07837-7_19</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036731464?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036731464?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 Mukku L., Christ (Deemed-to-be) University, Bengaluru, India; Burri V.R., Colorado State University, Fort Collins, CO, United States; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, India Description An account of the resource Sentiment analysis is a pivotal Natural Language Processing (NLP) task that enables the extraction of actionable insights from textual data, particularly social media. With the rise of public discourse on platforms like Twitter, analyzing sentiment trends has become crucial for decision-making in domains such as policy implementation, feedback evaluation, and public opinion monitoring. Mul-Sensis employs a hybrid approach combining transformer-based models with classical machine learning algorithms to enhance sentiment classification. The system integrates advanced preprocessing techniques to address linguistic complexities like sarcasm, idiomatic expressions, and domain-specific nuances. A robust hybrid annotation approach, incorporating both human expertise and machine-assisted methods, ensures high-quality, bias-free sentiment labeling. This study contributes a scalable, interpretable, and domain-agnostic framework for sentiment analysis, offering valuable insights for policymakers, researchers, and industries relying on textual data analytics. The findings highlight the transformative potential of hybrid and ensemble-based NLP approaches for understanding public sentiment across diverse cultural and linguistic contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource BERTweet; Ensemble learning; Hybrid transformers; Mul-Sensis; TextBlob; VADER Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-303207836-0; 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