Figure 1 – Social Media Landscape 2015 (Source: FredCavazza.net)

Slides:



Advertisements
Similar presentations
Barriers- ability to address complex scientific dilemmas Disciplinary specialization- does not guarantee the ability to solve complex problems To cross.
Advertisements

Strategic decision making with exploratory search Toby Mostyn CTO Polecat.
– European-level thematic State of Environment (SOE) information
Supplemental Figure 1 A No. at risk T T T
New Technologies Supporting Technical Intelligence Anthony Trippe, 221 st ACS National Meeting.
2015 SLA IT Webinar Using Analytics to Understand Social Media Activity Michelle Chen School of Information San José State University February 4 th, 2015.
SOCIAL MEDIA FOR CONSUMER INSIGHT Chapter Chapter Objectives  Describe the types of data used in social media research  Explain the different.
1 Causal Analytics with Social Media Content Lipika Dey Innovation Labs, Delhi.
© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,
© Tan,Steinbach, Kumar Introduction to Data Mining 1/17/ Data Mining Cluster Analysis: Basic Concepts and Algorithms Figures for Chapter 8 Introduction.
© 2002 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin TURNING MARKETING INFORMATION INTO ACTION.
Classical Techniques: Statistics, Neighborhoods, and Clustering.
Atmosphere. Biosphere Character of a Place Command Economy.
Digital Repositories and Social Science Data: Supporting the Data Life Cycle IASSIST 2006 Panel Discussion Ann Green, Chair Ann Arbor May 24, 2006.
P247. Figure 9-1 p248 Figure 9-2 p251 p251 Figure 9-3 p253.
Mobile Photos April 17, Auto Extraction of Flickr Tags Unstructured text labels Extract structured knowledge Place and event semantics Scale-structure.
Aim of paper To investigate teachers’ perceptions on the role that teachers’ associations play in their professional development, with reference to the.
Data Mining: Concepts & Techniques. Motivation: Necessity is the Mother of Invention Data explosion problem –Automated data collection tools and mature.
Mobility analysis from Twitter data NTTS satellite Workshop on Big Data.
Data Mining Techniques
Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
USGBC Federal Summit, Washington, DC 29 April 2003 TOOLS: The Latest in Green Specs and Life Cycle Assessment Alison Kinn Bennett US EPA Environmentally.
DECISION SUPPORT SYSTEM ARCHITECTURE: The data management component.
(8) Geography. The student uses geographic tools to collect, analyze, and interpret data. The student is expected to: (A) create thematic maps, graphs,
Defining Text Mining Preprocessing Transforming unstructured data stored in document collections into a more explicitly structured intermediate format.
Chapter Four Chapter Four.
Automatic Detection of Tags for Political Blogs Khairun-nisa Hassanali Vasileios Hatzivassiloglou The University.
Collecting Secondary Data
The Legal Framework. Topics covered in this presentation Concepts of law Relevant bodies of International Law National Law.
From Science to Policy Making: Investigating the Use and Influence of Marine Environmental Grey Literature B.H. MacDonald, P.G. Wells, R.E. Cordes, G.R.G.
Last Words DM 1. Mining Data Steams / Incremental Data Mining / Mining sensor data (e.g. modify a decision tree assuming that new examples arrive continuously,
DATA COLLECTION DATA COLLECTION By Najma Khan By Najma Khan Khurram Anwar Khurram Anwar.
HISTORICAL THINKING A lesson on WHY and HOW we study history.
Choosing an issue, gathering research.  Any matter that causes people to become concerned and about which there are several points of view  An issue.
Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : YUNG-MING LI, TSUNG-YING LI 2013, DSS Deriving market intelligence from microblogs.
INFORMATION RETRIEVAL PROJECT Creation of clusters of concepts that represent a domain corpus.
Introduction to Data Mining by Yen-Hsien Lee Department of Information Management College of Management National Sun Yat-Sen University March 4, 2003.
Chapter 10: Tourism Entrepreneurship and Social Capital.
22/10/091 PRODUCTION OF MUNICIPAL SCHOOL ATLASES IN THE STATE OF SÃO PAULO, BRAZIL Rosangela Doin de Almeida
Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.
Chapter Four Chapter 4 Exploratory Research Design: Secondary Data.
Content Analytics – Uncovering Critical Insight YellowBrix 3/2/20161.
Market Research and Resource Assessment L 3 B Ing. Jiří Šnajdar 2016.
NERI Roskilde Tuesday, May 18 th 2004 EEA activities and projects on spatial analysis and land accounting Jean-Louis Weber, EEA LANDSCAPE EUROPE Seminar.
Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods.
Chapter 3: Marketing Research Chapter 3 Lesson 1 Marketing Research: Role and Scope pp pp Pearson Education Canada1.
CS570: Data Mining Spring 2010, TT 1 – 2:15pm Li Xiong.
Topic 4: Cluster Analysis Analysis of Customer Behavior and Service Modeling.
Part 2: Planning and Strategy Chapter 6
中国计算机学会学科前沿讲习班:信息检索 Course Overview
Topic 3: Cluster Analysis
Mining the Data Charu C. Aggarwal, ChengXiang Zhai
Data Mining: Concepts and Techniques Course Outline
Colors.
What is Pattern Recognition?
Basic Economic Concepts
4. Vocabulary building in relation to “Royal” in relation to “Divorce”
NETWORK-BASED MODEL OF LEARNING
Chapter 4 - Case Study Clustering
Primary and Secondary Data
Master Dissertation Proposals
Course Summary ChengXiang “Cheng” Zhai Department of Computer Science
Chapter 2 Useful Tools and Concepts.
Spatial Data Mining Definition: Spatial data mining is the process of discovering interesting patterns from large spatial datasets; it organizes by location.
PolyAnalyst Web Report Training
Sentiment Analysis In Student Learning Experience By Obinna Obeleagu
Sentiment Analysis In Student Learning Experience By Obinna Obeleagu
WRT 205: critical research
Asst. Prof. Sotarat Thammaboosadee, Ph.D.
Presentation transcript:

Figure 1 – Social Media Landscape 2015 (Source: FredCavazza.net)

Social View 360 Metrics Text Pre-Processing and Data Collection Insights Apply Data Extraction Algorithms Statistical Analysis, Pattern Recognition and Data Modeling Database Creation - Identify Concepts, Entities, Patterns, Related to 4 Metrics Processing and Analysis Four Metrics Governance Environment Well Being Economics Figure 2 – Methodology and Work Flow

Figure 3a (top) – Regional and Primary Clusters of Topics in Regions; Figure 3b –Secondary Clustering of Themes of the Various Reports

Figure 4 – Positive (green) and Negative (red) Sentiment Concerning Governance, Helath, Knowledge and Technology, and Environmental Risk Based on Selected Published Reports (2012)

Figure 5– Positive (green) and Negative (red) Sentiment Concerning Social Entrepreneurship, Public-private Partnerships, and Governance Based on Selected Published Reports (2012)