國立虎尾科技大學 |

Advanced applications of NLP and deep learning in social media data

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Advanced applications of NLP and deep learning in social media data/ Ahmed A. Abd El-Latif, Mudasir Ahman Wani, and Mohammed El-Affendi.
其他作者:	El-Affendi, Mohammed,
出版者:	Hershey, Pennsylvania :IGI Global, : 2023.,
面頁冊數:	1 online resource (303 p.)
標題:	Social media. -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6909-5
ISBN:	9781668469118 (electronic bk.)

Advanced applications of NLP and deep learning in social media data
Advanced applications of NLP and deep learning in social media data[electronic resource] /Ahmed A. Abd El-Latif, Mudasir Ahman Wani, and Mohammed El-Affendi. - Hershey, Pennsylvania :IGI Global,2023. - 1 online resource (303 p.)

Includes bibliographical references and index.

Chapter 1. A comprehensive study to detect social spam campaigns -- Chapter 2. ARIMA modeling and forecasting of COVID-19 second wave in the 10 most affected states of India -- Chapter 3. Clustering methods and tools to handle high-dimensional social media text data -- Chapter 4. Community detection algorithms: a critical review -- Chapter 5. Emotion recognition from text using multi-head attention-based bidirectional long short-term memory architecture using multi-level classification -- Chapter 6. Learning from small samples in the age of big data -- Chapter 7. LSTM network: a deep learning approach and applications -- Chapter 8. Machine learning-based prediction of users' involvement on social media -- Chapter 9. NLP techniques and challenges to process social media data -- Chapter 10. Trolling, cyber-stalking, cyber-bullying, and identity theft in OSNs: methods, issues, and challenges -- Chapter 11. Uncertainty quantification in advanced machine learning approaches.

"The primary objective of this book is to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. This book bridges the gap between Natural Language Processing (NLP), Advanced Machine(AML) and Deep Learning (DL), and Online Social Media. This book connects various interdisciplinary domains related to Natural Language Understanding, Deep machine Leaning Technology and will be highly beneficial for the students, researchers, and academicians working in this area as this book will cover state-of-the-art technologies around NLP and DML techniques and their role in Social Media Data Analysis. Furthermore, the OSN service providers will take the advantage of this book to update, modify and make better social platforms for its users. Psychiatrists and clinicians will also be beneficial as this book's main focus are to analyze the user behavior in Online Social networks which play a key ingredient in several psychological tests"--

ISBN: 9781668469118 (electronic bk.)Subjects--Topical Terms:

780265
Social media.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA76.9.N38 / A38 2023e

Dewey Class. No.: 006.3/5

Advanced applications of NLP and deep learning in social media data
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245 0 0 $a Advanced applications of NLP and deep learning in social media data $h [electronic resource] / $c Ahmed A. Abd El-Latif, Mudasir Ahman Wani, and Mohammed El-Affendi.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2023.
300 $a 1 online resource (303 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. A comprehensive study to detect social spam campaigns -- Chapter 2. ARIMA modeling and forecasting of COVID-19 second wave in the 10 most affected states of India -- Chapter 3. Clustering methods and tools to handle high-dimensional social media text data -- Chapter 4. Community detection algorithms: a critical review -- Chapter 5. Emotion recognition from text using multi-head attention-based bidirectional long short-term memory architecture using multi-level classification -- Chapter 6. Learning from small samples in the age of big data -- Chapter 7. LSTM network: a deep learning approach and applications -- Chapter 8. Machine learning-based prediction of users' involvement on social media -- Chapter 9. NLP techniques and challenges to process social media data -- Chapter 10. Trolling, cyber-stalking, cyber-bullying, and identity theft in OSNs: methods, issues, and challenges -- Chapter 11. Uncertainty quantification in advanced machine learning approaches.
520 $a "The primary objective of this book is to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. This book bridges the gap between Natural Language Processing (NLP), Advanced Machine(AML) and Deep Learning (DL), and Online Social Media. This book connects various interdisciplinary domains related to Natural Language Understanding, Deep machine Leaning Technology and will be highly beneficial for the students, researchers, and academicians working in this area as this book will cover state-of-the-art technologies around NLP and DML techniques and their role in Social Media Data Analysis. Furthermore, the OSN service providers will take the advantage of this book to update, modify and make better social platforms for its users. Psychiatrists and clinicians will also be beneficial as this book's main focus are to analyze the user behavior in Online Social networks which play a key ingredient in several psychological tests"-- $c Provided by publisher.
650 0 $a Social media. $3 780265
650 0 $a Web usage mining. $3 670076
650 0 $a Sentiment analysis. $3 1403973
650 0 $a Semantic computing. $3 772728
650 0 $a Deep learning (Machine learning) $3 1381171
650 0 $a Natural language processing (Computer science) $3 641811
655 4 $a Electronic books. $2 local $3 554714
700 1 $a El-Affendi, Mohammed, $d 1971-. $3 1427657
700 1 $a Wani, Mudasir Ahman, $d 1987-. $3 1427656
700 1 $a Abd El-Latif, Ahmed A., $d 1984-. $3 1427655
710 2 $a IGI Global. $3 805187
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6909-5