A Streamlined Client-Server Architecture for Sustainable Sentiment Analysis System Using Textual Data

This work presents a comprehensive sustainable sentiment analysis system utilizing textual data, designed within a structured client-server architecture for real-time deployment. The system integrates dual feature representations Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) whose prediction scores are combined through a parameter-free score-level fusion strategy. The implementation of the proposed system consists of five major components. The first component involves the acquisition of textual data from various sources, followed by rigorous text preprocessing to eliminate noise and enhance data quality. The second component focuses on feature extraction, ensuring that the extracted features not only reduce computational overhead but also retain high discriminative capability to effectively represent sentiments. The third component involves the development of sentiment classification models to categorize textual data based on sentiment polarity. The framework is evaluated across binary (2-class), ternary (3-class), and fine-grained (13-class) sentiment and emotion datasets. To address class imbalance in the 13-class setting, LLM-based data augmentation is incorporated during training. Following model development, the fourth component involves performance optimization, where the best-performing feature models are selected, and classifier parameters are fine-tuned to maximize accuracy and efficiency. Finally, in the fifth component, the optimized and scalable sentiment analysis models for both three-class and thirteen-class sentiment categories are deployed to a server environment for sustainable real-time sentiment classification tasks. Experimental results demonstrate that the proposed architecture maintains stable predictive performance while supporting controlled vocabulary sizes for efficient deployment. The system design emphasizes thin-client interaction, stateless REST-based inference, and centralized model management, ensuring practical applicability in real-world environments, making the system suitable for large-scale implementation in various domains such as customer feedback analysis, social media monitoring, and opinion mining.