DEEP LEARNING METHODS FOR SENTIMENT ANALYSIS USING BERT AND GRU MODELS - Najeem Olawale Adelakun
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"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 ... Pilns apraksts
Aprašymas
"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.
Vairāk informācijas
| Autors | Najeem Olawale Adelakun |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2024 |
| Vāka tips | Mīkstais vāks |
| EAN | 9786208012793 |