Mul-Sensis: Multilingual Sentiment Analysis Framework for Emotion Detection

Title

Mul-Sensis: Multilingual Sentiment Analysis Framework for Emotion Detection

Creator

Mukku, Lalasa; Burri, Vikas Reddy; Lamani, Manjunath Ramanna

Description

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.

Source

Smart Innovation, Systems and Technologies;Volume;454 SIST;pp.237-247

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

BERTweet; Ensemble learning; Hybrid transformers; Mul-Sensis; TextBlob; VADER

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 21903018; ISBN: 978-303207836-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-07837-7_19

https://www.scopus.com/pages/publications/105036731464?origin=resultslist