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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
"Deep Learning Methods for Sentiment Analysis Using BERT & GRU Models" addresses the need for advanced sentiment analysis in financial markets, where traditional tools often fail to capture complex market emotions.This book presents deep learning techniques with Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT), offering enhanced analysis of sentiment patterns in financial data. It outlines a methodical approach involving data gathering, preprocessing, downsampling, and sentiment classification, using GRU and BERT for precise results.The book is ideal for finance, data science, and AI professionals, the book provides valuable insights to improve sentiment analysis, benefiting those engaged in financial market analysis, investment strategies, and natural language processing, and aims to enhance decision-making and strategic planning.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
"Deep Learning Methods for Sentiment Analysis Using BERT & GRU Models" addresses the need for advanced sentiment analysis in financial markets, where traditional tools often fail to capture complex market emotions.This book presents deep learning techniques with Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT), offering enhanced analysis of sentiment patterns in financial data. It outlines a methodical approach involving data gathering, preprocessing, downsampling, and sentiment classification, using GRU and BERT for precise results.The book is ideal for finance, data science, and AI professionals, the book provides valuable insights to improve sentiment analysis, benefiting those engaged in financial market analysis, investment strategies, and natural language processing, and aims to enhance decision-making and strategic planning.