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Social big data analytics = practice...

Abu-Salih, Bilal.

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Social big data analytics = practices, tchniques, and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Social big data analytics/ by Bilal Abu-Salih ... [et al.].
Reminder of title:	practices, tchniques, and applications /
other author:	Abu-Salih, Bilal.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	x, 218 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-981-33-6652-7
ISBN:	9789813366527

Social big data analytics = practices, tchniques, and applications /
Social big data analyticspractices, tchniques, and applications /[electronic resource] :by Bilal Abu-Salih ... [et al.]. - Singapore :Springer Singapore :2021. - x, 218 p. :ill., digital ;24 cm.

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:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Social big data analytics = practices, tchniques, and applications /
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245 0 0 $a Social big data analytics $h [electronic resource] : $b practices, tchniques, and applications / $c by Bilal Abu-Salih ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a x, 218 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Big data. $3 2045508
650 0 $a Online social networks. $3 624374
650 0 $a Marketing $x Data processing. $3 582001
650 0 $a Internet marketing. $3 570186
650 0 $a Marketing research. $3 532352
650 0 $a Data mining. $3 562972
650 0 $a Machine learning. $3 533906
650 1 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Online Marketing/Social Media. $3 2181685
650 2 4 $a Market Research/Competitive Intelligence. $3 2162287
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Abu-Salih, Bilal. $3 3492155
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-33-6652-7
950 $a Business and Management (SpringerNature-41169)