國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep learning-based approaches for s...

Agarwal, Basant.

Deep learning-based approaches for sentiment analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning-based approaches for sentiment analysisedited by Basant Agarwal ... [et al.].
其他作者:	Agarwal, Basant.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xii, 319 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Natural language processing (Computer science)
電子資源:	https://doi.org/10.1007/978-981-15-1216-2
ISBN:	9789811512162$q(electronic bk.)

Deep learning-based approaches for sentiment analysis
Deep learning-based approaches for sentiment analysis[electronic resource] /edited by Basant Agarwal ... [et al.]. - Singapore :Springer Singapore :2020. - xii, 319 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..

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.

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.

ISBN: 9789811512162$q(electronic bk.)

Standard No.: 10.1007/978-981-15-1216-2doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38 / D447 2020

Dewey Class. No.: 006.35

Deep learning-based approaches for sentiment analysis
LDR:02571nmm a2200349 a 4500 001 573541
003 DE-He213
005 20200620134609.0
006 m d
007 cr nn 008maaau
008 200928s2020 si s 0 eng d
020 $a 9789811512162$q(electronic bk.)
020 $a 9789811512155$q(paper)
024 7 $a 10.1007/978-981-15-1216-2 $2 doi
035 $a 978-981-15-1216-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.N38 $b D447 2020
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 006.35 $2 23
090 $a QA76.9.N38 $b D311 2020
245 0 0 $a Deep learning-based approaches for sentiment analysis $h [electronic resource] / $c edited by Basant Agarwal ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xii, 319 p. : $b ill., digital ; $c 24 cm.
490 1 $a Algorithms for intelligent systems, $x 2524-7565
505 0 $a 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.
520 $a 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.
650 0 $a Natural language processing (Computer science) $3 200539
650 0 $a Data mining. $3 184440
650 0 $a Machine learning. $3 188639
650 1 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
650 2 4 $a Natural Language Processing (NLP) $3 826373
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
700 1 $a Agarwal, Basant. $3 738691
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Algorithms for intelligent systems. $3 857955
856 4 0 $u https://doi.org/10.1007/978-981-15-1216-2
950 $a Engineering (Springer-11647)