A Semantically Enriched Hybrid Learning Paradigm for Large-Scale Opinion Mining in Geopolitical Crisis Narratives

Abstract

Among the more than 500 million tweets generated daily, a considerable number reflect public perspectives on global socio-political events such as the Russia–Ukraine War. Analyzing sentiment at this scale manually is time-consuming, inconsistent, and unsuitable for real-time decision-making. To overcome these limitations, this study presents a hybrid deep learning framework for automated sentiment analysis of Twitter discussions related to the Russia–Ukraine conflict. The approach begins with comprehensive Natural Language Processing (NLP) preprocessing, including tokenization, stopword removal, normalization, and lemmatization, followed by Exploratory Data Analysis (EDA) to analyze sentiment distribution, word frequency patterns, and textual trends. Context-aware semantic representations are generated using Distilled Robustly Optimized BERT Pretraining Approach (DistilRoBERTa), which offers efficient and lightweight transformer-based embeddings with reduced computational cost. To handle class imbalance and enhance minority class prediction, KMeans-Synthetic Minority Over Sampling Technique (SMOTE) is applied for synthetic data generation. For comparative evaluation, traditional classifiers such as Logistic Regression (LR), Support Vector Machine Classifier (SVC), and Random Forest Classifier (RFC) are utilized. The proposed model combines a Deep Neural Network (DNN) for effective feature learning with a Greedy Tree-based classifier (GTC) to improve classification performance and generalization capability. The resulting system, named “Semantic Distil Deep Tree (SDDT),” demonstrates superior performance over baseline models in terms of accuracy and F1-score. Overall, the proposed framework delivers a scalable, efficient, and dependable solution for real-time sentiment monitoring, aiding policymakers, researchers, and analysts in understanding public opinion on global conflicts.