Deep Learning for Sentiment Analysis : A Survey

TL;DR

This survey reviews how deep learning techniques have been increasingly applied to sentiment analysis, highlighting recent advancements and current state-of-the-art methods in the field.

Contribution

It provides a comprehensive overview of deep learning applications in sentiment analysis, summarizing recent developments and trends.

Findings

Deep learning models achieve high accuracy in sentiment classification.

Various neural network architectures are effective for sentiment analysis.

The survey identifies key challenges and future directions in the field.

Abstract

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.