國立虎尾科技大學 |

Social Big Data Analytics = Practices, Techniques, and Applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Social Big Data Analytics/ by Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu, Kit Yan Chan, Amit Rudra.
其他題名:	Practices, Techniques, and Applications /
作者:	Abu-Salih, Bilal.
其他作者:	Rudra, Amit.
面頁冊數:	X, 218 p. 88 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-981-33-6652-7
ISBN:	9789813366527

Social Big Data Analytics = Practices, Techniques, and Applications /
Abu-Salih, Bilal.

Social Big Data AnalyticsPractices, Techniques, and Applications /[electronic resource] :by Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu, Kit Yan Chan, Amit Rudra. - 1st ed. 2021. - X, 218 p. 88 illus.online resource.

Chapter 1. Social Big Data: An Overview and Applications -- Chapter 2. Introduction to Big data Technology -- Chapter 3. Credibility Analysis in Social Big Data -- Chapter 4. Semantic data discovery from Social Big Data -- Chapter 5. Predictive analytics using Social Big Data and machine learning -- Chapter 6. Affective Design Using Social Big Data -- Chapter 7. Sentiment Analysis on Big News Media Data.

This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display.

ISBN: 9789813366527

Standard No.: 10.1007/978-981-33-6652-7doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: HF5548.125-5548.6

Dewey Class. No.: 658.4038

Social Big Data Analytics = Practices, Techniques, and Applications /
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