Exploring Sentiment Analysis through Deep Learning: A Comprehensive Review

This survey paper provides an in-depth exploration of sentiment analysis on social media through the lens of deep learning-based methods. It systematically examines various components essential to sentiment analysis, including data classification, pre-processing techniques, text representations, and learning models, as well as their applications as discussed in different research papers. The authors evaluate recent advancements in deep learning architectures, highlighting their advantages and disadvantages in the context of sentiment analysis. Additionally, the paper addresses the challenges faced in this field, such as the informal nature of social media language and the necessity for large and labelled datasets, while also identifying factors that can enhance the accuracy of sentiment classification. A significant focus of the article is on emotion recognition from text, which is recognized as a critical task within Natural Language Processing (NLP) that can greatly benefit artificial intelligence and improve human interaction. To illustrate the effectiveness of deep learning techniques, the authors propose a sentiment classification method utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, demonstrating high accuracy in emotion classification across three different datasets. Overall, the paper offers valuable insights into the current state of sentiment analysis, emphasizing the potential of deep learning to advance this important area of research. Recurrent Neural Network (RNN). Gated Recurrent Unit (GRU) was reported as the best performer in terms of accuracy on benchmark datasets.