Keywords
Opinion mining
Deep learning
Explainable AI
Multilingual NLP
How to Cite
Abstract
Purpose: This paper provides a comprehensive review of current methods, progress, and open challenges in Sentiment Analysis (SA)—a computational discipline for extracting and interpreting opinions, sentiments, and emotions from text. It aims to identify the limitations of existing approaches and explore pathways toward more generalizable, explainable, and ethically robust sentiment analysis models.
Design/Methodology/Approach: The study synthesizes insights from a tertiary review (Lighart et al., 2021), which aggregates findings from 14 systematic literature reviews and mapping studies, and a domain-specific review (Sweta, 2024) focusing on SA in educational contexts. A thematic analysis integrates methodological evolution, major challenges, and emerging trends to produce a consolidated overview of the field’s trajectory.
Findings: Results indicate a clear shift from lexicon-based and traditional machine learning approaches to deep learning and transformer-based architectures (e.g., LSTM, CNN, BERT, GPT). Despite significant progress, persistent issues remain—such as domain and language dependency, contextual subtlety (sarcasm, irony), data imbalance, lack of explainability, and ethical concerns regarding privacy. New trends, including cross-domain transfer learning, explainable AI (XAI), multimodal sentiment analysis, and real-time processing, hold promise for overcoming these barriers.
Research Limitations/Implications: The review highlights the need for standardized datasets, cross-lingual benchmarks, and interdisciplinary collaboration between NLP researchers and domain experts to enhance model robustness and ethical compliance.
Originality/Value: By integrating high-level evidence from tertiary research with practical insights from domain-specific studies, this paper outlines the current landscape and future directions for building generalizable, transparent, and ethically grounded sentiment analysis systems.
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