Sentiment analysis using deep learning techniques: a comprehensive review

With the exponential growth of social media platforms and online communication, the necessity of using automated sentiment analysis techniques has significantly increased. Deep learning techniques have emerged in extracting complex patterns and features from unstructured text data, which makes them a powerful tool for sentiment analysis. This research article presents a comprehensive review of sentiment analysis using deep learning techniques. We discuss various aspects of sentiment analysis, including data preprocessing, feature extraction, model architectures, and evaluation metrics. We explore the use of recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models in sentiment analysis tasks. We examine the utilization of RNNs, incorporating long short-term memory (LSTM) and gated recurrent unit (GRU), to model sequential dependencies in text data. Furthermore, we discuss the recent advancements in sentiment analysis achieved through a transformer. The findings from this review can facilitate the development of more accurate and efficient sentiment analysis models, enabling organizations to gain valuable insights from large volumes of textual data in several domains, such as social media, market analysis, and customer reviews.

Automated summary

This research article provides a comprehensive review of using deep learning techniques in sentiment analysis. It covers various aspects of sentiment analysis, including data preprocessing, feature extraction, model architectures, and evaluation metrics. The study explores the application of recurrent neural networks, convolutional neural networks, and transformer models in sentiment analysis, as well as the utilization of long short-term memory and gated recurrent unit to model sequential dependencies in text data. The findings from this review can aid in the development of more accurate and efficient sentiment analysis models, benefiting organizations in gaining insights from large volumes of textual data in various domains.