A Review of Attention Based Model for Sentimental Analysis using NLP

Attention mechanisms are an important part of deep learning models that help in handling sequential data. They provide a flexible and effective way to deal with sequential data by focusing on relevant information. This mechanism is widely used in various deep learning architectures and is essential in natural language processing tasks like sentiment analysis. Attention mechanisms address the problem of traditional sequence-to-sequence models. The purpose of this paper is to provide an overview of the latest attention mechanisms in sentiment analysis and to analyze various models for attention in natural language processing. This work investigates the performance of various attention network models and analyzes their accuracy in sentiment polarity detection. Additionally, the paper examines diverse datasets used in sentiment analysis, highlights research gaps, and outlines potential future directions in the research area.