Survey Paper: Fine-Grained Sentiment Analysis Using Attention Mechanisms in Machine Learning and Deep Learning

Fine-grained sentiment analysis has gained significant attention in natural language processing (NLP) due to its ability to capture nuanced sentiment, such as emotional shades and aspect-level sentiments. Recent advancements in machine learning (ML) and deep learning (DL) have leveraged attention mechanisms to improve the performance of these tasks. Attention mechanisms enable models to focus on the most relevant parts of input text, enhancing their ability to perform tasks like aspect-based sentiment analysis and fine-grained classification. This survey paper discusses recent techniques, algorithms, and challenges in fine-grained sentiment analysis using attention mechanisms, highlighting studies from 2019 to 2024. The review also identifies gaps in current research and suggests potential directions for future studies.