Open Access

ARTICLE

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

Soumalya De¹, Rahil Akhtar², Saiyed Umer², Ranjeet Kumar Rout³, G. G. Md. Nawaz Ali^4,*

1 Department of Information Technology, Techno International New Town, Kolkata, India
2 Department of Computer Science and Engineering, Aliah University, Kolkata, India
3 Department of Information Technology, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India
4 Department of Computer Science and Information Systems, Bradley University, Peoria, IL, USA

* Corresponding Author: G. G. Md. Nawaz Ali. Email:

(This article belongs to the Special Issue: Sentiment Analysis for Social Media Data: Lexicon-Based and Large Language Model Approaches)

Computers, Materials & Continua 2026, 88(1), 35 https://doi.org/10.32604/cmc.2026.079340

Received 20 January 2026; Accepted 09 March 2026; Issue published 08 May 2026

Abstract

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.

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Keywords

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