Sentiment Analysis and Opinion Mining in Digital Environments Using Deep Learning: Evolution from Sequential Models to Large Language Models (LLMs)
DOI:
https://doi.org/10.55204/trc.v6i2.e726Keywords:
Sentiment analysis, Deep Learning, PRISMA methodology, Large language models, algorithmic explainabilityAbstract
Sentiment analysis in digital environments has undergone a rapid methodological evolution, transitioning from classic sequential models to deep learning architectures capable of understanding profound human linguistic context. To examine this progression, a systematic review was conducted applying the PRISMA protocol, evaluating high-impact empirical studies (2020-2025) extracted from Scopus, IEEE Xplore, Web of Science, etc. The findings validate the efficacy of these technologies in real-world scenarios, highlighting their use in e-commerce satisfaction monitoring and public health trend assessment. However, the review reveals a priority research gap: the urgency of integrating algorithmic explainability (XAI) from the system's conception and democratizing these tools for low-resource languages. It is concluded that the current state of the art lies in combining Transformers and Large Language Models (LLMs) with parameter-efficient adaptation techniques (such as LoRA), offering organizations an optimal balance between maximum predictive accuracy and computational viability.Downloads
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