Kazem Taghandiki - Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran
Mohsen Ehtesham - Chairman of the Board, Tizpak Khorasan Company

Sentiment analysis, the process of identifying and extracting sentiment or emotion from text, has gained significant attention in recent years due to its wide-ranging applications in fields such as marketing, customer service, and social media analysis. With the advent of deep learning, particularly transfer learning networks, sentiment analysis has seen remarkable advancements in accuracy and efficiency. This review article provides a comprehensive overview of sentiment analysis with transfer learning networks.We begin by introducing the concept of sentiment analysis and its relevance in the context of deep learning. We then delve into the history and evolution of sentiment analysis techniques, highlighting the emergence of transfer learning networks as a powerful tool in this domain. The architecture of transfer learning networks, with a focus on Transformer models such as BERT and GPT, is elucidated, along with the data preprocessing steps required for their effective utilization. Furthermore, we discuss various training methods employed for sentiment analysis tasks and evaluate the performance of transfer learning models in sentiment detection and interpretation. Applications and case studies across diverse domains showcase the practical utility of these models. We also address existing challenges and recent advancements in sentiment analysis with transfer learning networks, offering insights into future research directions. This review serves as a valuable resource for researchers, practitioners, and enthusiasts interested in understanding the state-of-the-art techniques and applications of sentiment analysis leveraging transfer learning networks. The most important factor in presenting this article is the opportunity provided by the studies conducted at Tizpak Khorasan Company.

Sentiment Analysis, Deep Learning, Transfer Learning Networks, Natural Language Processing