Deep Learning-Based Approaches for Sentiment Analysis / / edited by Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik

• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (XII, 319 p.)
• Disciplina: 006.31
• Soggetto topico: Signal processing; Data mining; Image processing - Digital techniques; Computer vision; Natural language processing (Computer science); Computational intelligence; Neural networks (Computer science); Signal, Speech and Image Processing; Data Mining and Knowledge Discovery; Computer Imaging, Vision, Pattern Recognition and Graphics; Natural Language Processing (NLP); Computational Intelligence; Mathematical Models of Cognitive Processes and Neural Networks
• Persona (resp. second.): AgarwalBasant; NayakRichi; MittalNamita; PatnaikSrikanta
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Chapter 1. Application of Deep Learning Approaches for Sentiment Analysis: A Survey -- Chapter 2. Recent Trends and Advances in Deep Learning based Sentiment Analysis -- Chapter 3. - Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews -- Chapter 4. Toxic Comment Detection in Online Discussions -- Chapter 5. Aspect Based Sentiment Analysis of Financial Headlines and Microblogs -- Chapter 6. Deep Learning based frameworks for Aspect Based Sentiment Analysis -- Chapter 7. Transfer Learning for Detecting Hateful Sentiments in Code Switched Language -- Chapter 8. Multilingual Sentiment Analysis -- Chapter 9. Sarcasm Detection using deep learning -- Chapter 10. Deep Learning Approaches for Speech Emotion Recognition -- Chapter 11. Bidirectional Long Short Term Memory Based Spatio-Temporal In Community Question Answering -- Chapter 12. Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language.
• Sommario/riassunto: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. .
• Titolo autorizzato: Deep Learning-Based Approaches for Sentiment Analysis
• ISBN: 981-15-1216-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910373905203321
• Lo trovi qui: Univ. Federico II
• Serie: Algorithms for Intelligent Systems, . 2524-7573