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E-raamat: Social Data Analytics [Taylor & Francis e-raamat]

(Uni.of Bergen), (Macquarie Uni.), (Macquarie Uni.), (Macquarie Uni.)
  • Formaat: 238 pages, 4 Tables, black and white; 3 Line drawings, color; 3 Line drawings, black and white; 5 Halftones, color; 5 Halftones, black and white; 8 Illustrations, color; 8 Illustrations, black and white
  • Ilmumisaeg: 01-Aug-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003260141
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 240,04 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 342,91 €
  • Säästad 30%
Social Data AnalyticsSuurem pilt
  • Formaat: 238 pages, 4 Tables, black and white; 3 Line drawings, color; 3 Line drawings, black and white; 5 Halftones, color; 5 Halftones, black and white; 8 Illustrations, color; 8 Illustrations, black and white
  • Ilmumisaeg: 01-Aug-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003260141
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003260141

This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics. It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry. It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics.



The book covers a large body of knowledge to help practitioners and researchers understand the problems, concepts, methods and tools involved in modern social data analytics. The book includes a wealth of material to choose from for courses in data science and analytics.

Dedication iii
Foreword iv
Preface v
1 Social Data Analytics: Challenges and Opportunities
1(23)
1.1 Understanding Social Data
1(2)
1.2 Organizing Social Data
3(4)
1.2.1 Social Data Volume
4(1)
1.2.2 Social Data Variety
4(1)
1.2.3 Social Data Velocity
5(1)
1.2.4 Social Data and Metadata
6(1)
1.3 Curating Social Data
7(1)
1.4 Processing Social Data
7(2)
1.5 Summarizing Social Data
9(2)
1.6 Storytelling with Social Data
11(1)
1.7 Social Media Text Analytics
12(1)
1.8 Social Image and Video Data Analytic
13(3)
1.9 The Future of Personalization
16(2)
1.10 Social Data Analytics Applications
18(3)
1.11 Goals, Structure, and Organization
21(3)
2 Organizing Social Data
24(18)
2.1 From Data to Big Data
24(5)
2.1.1 Big Data
25(1)
2.1.2 NoSQL: The Need for New Database Management Systems
26(3)
2.2 Capturing Social Data
29(2)
2.3 Organizing Social Data
31(1)
2.4 Warehousing Social Data
32(1)
2.5 Social Data Provenance
33(3)
2.5.1 Provenance Representation
35(1)
2.5.2 Temporal Databases and Graphs
36(1)
2.6 Data Lakes
36(4)
2.6.1 Data Lake as a Service
37(1)
2.6.2 Index and Federated Search
38(2)
2.6.3 Security and Access Control
40(1)
2.7 Concluding Remarks and Discussion
40(2)
3 Curating Social Data
42(20)
3.1 Social Data Curation: Cleaning, Integration, and Transformation
42(7)
3.1.1 Identifying Relevant Data Sources
43(1)
3.1.2 Ingesting Data and Knowledge
44(1)
3.1.3 Data Cleaning
45(2)
3.1.4 Data Integration
47(1)
3.1.5 Data Transformation
48(1)
3.2 Social Data Curation: Adding Value
49(9)
3.2.1 Extraction
51(3)
3.2.2 Correction and Enrichment
54(2)
3.2.3 Linking
56(2)
3.2.4 Summarization
58(1)
3.3 Knowledge Lakes
58(1)
3.4 Concluding Remarks and Discussion
59(3)
4 Social Media Text Analytics
62(19)
4.1 Text Analytics: Overview
62(10)
4.1.1 Text Preprocessing
63(1)
4.1.2 Text Representation
64(5)
4.1.3 Knowledge Discovery
69(3)
4.2 Social Data Text Analytics: Challenges and Opportunities
72(2)
4.2.1 Time Sensitivity
72(1)
4.2.2 Format and Style
73(1)
4.3 Social Data Text Analytics
74(6)
4.3.1 Event Detection
74(1)
4.3.2 Social Data Tagging
75(1)
4.3.3 Topic Modeling
75(2)
4.3.4 Social Data Text Classification
77(1)
4.3.5 Sentiment and Opinion Extraction
78(1)
4.3.6 Linking Textual Data and Social Metadata
79(1)
4.4 Concluding Remarks and Discussion
80(1)
5 Social Media Image and Video Analytics
81(22)
5.1 Image and Video Analytic: Overview
81(2)
5.2 Image and Video Analytic: Opportunities and Challenges
83(3)
5.2.1 Opportunities
84(1)
5.2.2 Challenges
85(1)
5.3 Image and Video Detection and Recognition
86(8)
5.3.1 Object Detection in Images and Video Frames
86(6)
5.3.2 Face Detection and Recognition
92(2)
5.4 Storytelling with Image and Video Data
94(2)
5.4.1 Image and Video Captioning
94(2)
5.4.2 Location Identification
96(1)
5.5 3D Posts on Social Media
96(5)
5.5.1 3D Content Sharing
96(1)
5.5.2 Light Field Technology
97(4)
5.6 Concluding Remarks and Discussion
101(2)
6 Summarizing Social Data
103(20)
6.1 Automatic Text Summarization: Overview
103(2)
6.1.1 Text Summarization v. Text Compression
104(1)
6.2 Social Data Summarization: Challenges and Opportunities
105(1)
6.3 Social Data Summarization: Generic Approaches
106(10)
6.3.1 Abstractive Summarization
108(1)
6.3.2 Extractive Summarization
109(4)
6.3.3 Hybrid Extractive and Abstractive Summarization
113(1)
6.3.4 Structured Summarization
113(2)
6.3.5 Interactive and Personalized Summarization
115(1)
6.4 Micro-blog Data Summarization
116(4)
6.4.1 Time-aware Summarization
116(2)
6.4.2 Event-based Summarization
118(1)
6.4.3 Opinion-based Summarization
118(2)
6.5 Evaluation Techniques
120(1)
6.6 Concluding Remarks and Discussion
121(2)
7 Storytelling with Social Data
123(12)
7.1 Storytelling with Social Data: Overview
123(3)
7.1.1 Challenges and Opportunities
124(2)
7.2 Data-driven Storytelling via Visualization
126(2)
7.2.1 Defining Objectives and Knowing the Audience
127(1)
7.2.2 Identifying a Compelling Narrative
127(1)
7.2.3 Incorporating Key Elements
127(1)
7.2.4 Transparency
127(1)
7.2.5 Visualization Method
128(1)
7.3 Visualization Techniques
128(6)
7.3.1 Static Data Visualization
128(1)
7.3.2 Interactive Data Visualization
129(3)
7.3.3 Adaptive Data Visualization
132(2)
7.4 Concluding Remarks and Discussion
134(1)
8 Social Data and Recommender Systems: The Future of Personalization
135(17)
8.1 Introduction
135(5)
8.1.1 Overview of Recommendation Approaches
136(1)
8.1.2 Collaborative Filtering Approaches
137(2)
8.1.3 Content-Based Approaches
139(1)
8.2 Social Recommendation and Personalization
140(6)
8.2.1 Social Data
141(1)
8.2.2 Trust-aware Recommendation
142(1)
8.2.3 Context-aware Recommendation
143(1)
8.2.4 Temporal Recommendation
144(1)
8.2.5 Cross-Domain Recommendation
144(1)
8.2.6 Group Recommendation
145(1)
8.3 Bias in Social Recommendation
146(2)
8.4 Application Domains
148(3)
8.4.1 Video Domain
148(1)
8.4.2 Music Domain
148(1)
8.4.3 Fashion Domain
149(1)
8.4.4 Tourism Domain
150(1)
8.4.5 Food Domain
150(1)
8.5 Concluding Remarks and Discussion
151(1)
9 Social Data Analytics Applications
152(16)
9.1 Social Data and Trust
152(1)
9.2 Bias in Social Data
153(1)
9.3 Personality Detection from Social Data
154(2)
9.4 Sentiment Analysis of Social Data
156(1)
9.5 Personalization with Social Data
157(2)
9.6 Sales and Marketing: Creating Successful Campaigns with Social Media Marketing Analytics
159(1)
9.7 Influence Maximization: Identify Influencers for Brands and Industries
160(1)
9.8 Situational Awareness: Discover Trending Topics
161(2)
9.9 Social Media Information Discovery: From Topic Trends to Sentiment Ratio
163(1)
9.10 Linking Social Media Performance to Business and Revenue Growth
164(1)
9.11 Performance Analysis of the Industry
165(2)
9.12 Concluding Remarks and Discussion
167(1)
References 168(69)
Index 237
Amin Beheshti is Full Professor of Data Science and the Director of AI-enabled Processes Research Centre at Macquarie University, Sydney, Australia. Amin is also the head of the Data Analytics Research Lab and Adjunct Academic in Computer Science at UNSW Sydney. Amin completed his Ph.D. and Postdoc in Computer Science and Engineering at UNSW Sydney and holds both a master's and bachelor's degree in Computer Science, both with First Class Honours. Amin has been recognized as a high-quality researcher in Big-Data/Data/Process Analytics and served as Keynote Speaker, General-Chair, PC-Chair, Organisation-Chair, and program committee member of top international conferences. Amin has contributed to many research and industry projects, and currently leading over 20 large research projects with highprofile companies. He is the leading author of several books in data, social, and process analytics, co-authored with other high-profile researchers.

Samira Ghodratnama is Senior Applied Machine Learning Scientist at Grainger Technology Group, USA, working on real-world problems related to Natural Language Processing (NLP). She is also a research fellow at Macquarie University, Sydney, Australia and Arizona State University, USA, working on complex research problems in the area of text mining, machine learning, and information extraction. Samira has extensive experience building data-centric applications. She holds a Ph.D. in Computer Science from Macquarie University, Sydney, Australia.

Mehdi Elahi is Associate Professor at the University of Bergen, Norway and Adjunct Professor at the Norwegian School of Economics, one of the leading business schools in Europe. He also holds an Honorary Associate Professor position at Macquarie University, Sydney, Australia. He is a co-author and WP-Leader of a large-scale SFI project (budget: 26 Million Euro), where he collaborates with the largest international industry players in the Media sector in Norway. Mehdi obtained his Ph.D. degree in Computer Science. His research has mainly focused on AI, Data Science, and Cognitive Science and their potential industrial applications such as recommendation and personalization systems.

Helia Farhood is Research Fellow in Data Science at Macquarie University, Sydney, Australia. Helia holds a Ph.D. in Computer Systems and Artificial Intelligence (AI) from the University of Technology Sydney (UTS), Sydney, Australia. She also holds a master's degree in Artificial Intelligence and a bachelor's degree in Computer Engineering with First Class Honours. Helia has extensive experience in Image and Video Processing, and her Ph.D. research focused on 3D reconstruction using light field technology. Helias research interests include AI, Machine Learning, and Image Processing.
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