Name: Advanced Hybrid Frameworks for Aspect-Level Sentiment Analytics
SKU: 68908171
Price: 109.06 PEN
Availability: InStock
Author: Frown, Keneddy
ISBN: 9798240887987

portada Advanced Hybrid Frameworks for Aspect-Level Sentiment Analytics

Advanced Hybrid Frameworks for Aspect-Level Sentiment Analytics

Formato

Libro Físico

Editorial

Beakers Bay

Autor

Frown, Keneddy

Encuadernación

Tapa Blanda

ISBN13

9798240887987

Advanced Hybrid Frameworks for Aspect-Level Sentiment Analytics

Frown, Keneddy (Autor) · Beakers Bay · Tapa Blanda

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Reseña del libro "Advanced Hybrid Frameworks for Aspect-Level Sentiment Analytics"

Advanced Hybrid Frameworks for Aspect-Level Sentiment Analytics explores advanced computational approaches for analyzing opinions, emotions, and sentiments associated with specific aspects of products, services, topics, and entities. The book focuses on aspect-level sentiment analysis, where individual components of a text are identified and their corresponding sentiment orientations are determined. The book introduces key concepts in natural language processing, sentiment analysis, machine learning, and deep learning, with particular emphasis on hybrid frameworks that combine multiple computational techniques to improve sentiment classification and analytical performance. It discusses approaches for text preprocessing, aspect extraction, feature representation, sentiment classification, model development, and performance evaluation. Particular attention is given to the integration of machine learning and deep learning methods for extracting meaningful information from complex textual data. The book examines challenges such as contextual language, linguistic variation, ambiguous expressions, and the identification of sentiment toward specific aspects within a sentence or document. By bringing together aspect-level sentiment analytics and hybrid intelligent frameworks, this book provides a useful reference for students, researchers, data scientists, artificial intelligence professionals, and practitioners working in natural language processing, text mining, opinion mining, and data analytics. It is especially relevant to readers interested in developing intelligent systems for extracting fine-grained insights from large volumes of textual information.