A Review on Text Sentiment Analysis With Machine Learning and Deep Learning Techniques

Abstract

Automating sentiment analysis in texts has become an important task in recent years due to the exponential growth of user-generated content, including comments and opinions on products and services. This represents a valuable opportunity for businesses to glean insights into customer sentiment and, in turn, to refine their offerings. Motivated by this, the machine learning field has witnessed a surge of innovation, with an introduction of models and tools being introduced to streamline sentiment analysis. This paper offers a thorough review of the recent advancements in machine learning and deep learning approaches for text sentiment analysis. We propose a novel framework for studying these models, distinguishing them by their structural intricacies. Additionally, we delve into the challenges, prospects, and emerging directions in research, as illuminated by our framework. Consequently, this paper equips researchers with a detailed panorama of the cutting-edge machine learning methodologies for dissecting text sentiment, easing the way for future explorations in this vibrant field.

Keywords

• Machine learning
• deep learning
• word embedding
• text encoding
• sentiment analysis
• text classification