A SYSTEMATIC REVIEW OF SENTIMENT ANALYSIS: APPROACHES, IMPLEMENTATIONS, AND COMPARATIVE EVALUATIONS
DOI: https://doi.org/10.65725/JCISE/2/2/008
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)
ISSN: 3107-8168
Volume 2 Issue 2, Apr-Jun 2026

Abstract: 
This systematic review synthesizes findings from 166 scholarly articles on sentiment analysis published between 2015 and 2025. The study categorizes sentiment analysis methodologies into knowledge-based, statistical, machine learning, and hybrid approaches, with a focus on their theoretical underpinnings and real-world implementations. Applications are explored across diverse domains including e-commerce, healthcare, social media, and literature. A comparative evaluation of over 50 algorithms indicates the consistent superiority of transformer-based models such as BERT and RoBERTa, while Support Vector Machines (SVM) remain competitive in domain-specific contexts. Key challenges such as context dependency, multilingual processing, and real-time analysis are identified. Multimodal sentiment analysis and explainable AI model creation are potential avenues for future research. The paper features original flowcharts and comparative performance diagrams to offer a comprehensive and structured overview of the current state and future trends in sentiment analysis

Authors: Mathi S, Dr Asaithambi V, Saravanapriya S

Keywords: Sentiment Analysis, Opinion Mining, NLP, Machine Learning, Deep Learning, Algorithm Comparison, BERT, SVM, LSTM.