Year: 2026
Type: article
Abstract: Social media users increasingly communicate across language boundaries, often combining two or more languages within the same sentence and frequently writing Indian languages in Roman script. This creates a difficult setting for sentiment analysis because conventional monolingual models must cope with spelling variation, transliteration, informal vocabulary, emojis, abbreviations, language switching, and context-dependent sentiment. Large multilingual Transformer models have improved contextual understanding, but their computational cost can make deployment difficult on resource-constrained systems. This thesis proposes a lightweight Transformer-based framework for multilingual and code-mixed sentiment analysis. The framework uses a multilingual teacher model, language-aware normalization, and knowledge distillation to transfer contextual knowledge into a smaller student model. The study focuses primarily on Hindi-English (Hinglish) and Spanish-English (Spanglish) social-media text, using the SemEval-2020 Task 9 SentiMix corpora as a reproducible benchmark. The proposed framework is evaluated against classical machine-learning, recurrent neural-network, and full-size Transformer baselines. Evaluation is designed to consider not only accuracy and macro/weighted F1, but also inference latency, parameter count, memory consumption, robustness to noisy and code-mixed text, and the consistency of predictions across language pairs. An explainability component is also included so that important tokens contributing to a prediction can be inspected. The central research question is whether a substantially smaller Transformer can retain useful multilingual sentiment knowledge while reducing computational requirements. The thesis therefore treats sentiment analysis as both a language-understanding problem and an engineering deployment problem. The resulting framework is intended for applications such as social-media monitoring, customer-feedback analysis, multilingual communication systems, and other AI systems where language diversity and limited computing resources must be considered together. Keywords: Transformer, multilingual NLP, code-mixed text, sentiment analysis, knowledge distillation, Hinglish, Spanglish, lightweight AI, explainable AI, social media.
Author: vijay kumar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7222871747