Social Media Intelligence Text and Network Mining combined. Dr. Rosaria Silipo
|
|
- Garey Nelson
- 5 years ago
- Views:
Transcription
1 Social Media Intelligence Text and Network Mining combined Dr. Rosaria Silipo
2 Previously on PAW... PAW San Francisco
3 Social Media Analysis Water Water Everywhere, and not a drop to drink Approaches and Challenges: In-House Text Mining: Sentiment but no relevance In-House Network Mining: Relevance but no Sentiment In-House Scorecard: No Analytics Cloud-based Approach: No Access to Data 3
4 Our Goal in Social Media Analysis Text Mining for Sentiment Network Mining for Relevance Drill Down on special cases Analytics for Prediction 4
5 Case Study: Major European Telco Very rich new data sources about customers! Combine Text mining Network Analysis Classic Predictive Analytics Modeling, Clustering, Time Series, etc Combine with internal Data makes the text relevant Include Product names/categories exclude Staff Members Include number of web hits per page... Include existing marketing positioning Include major campaign information 5
6 Case Study Example: Slashdot Data News for Nerds, Stuff that Matters Basic Facts: users 491 threads with responses from users posts (text mining on posts) 60 main topics 6
7 Combining Text and Network Mining Network Analysis Hub and Authority Score per User Text Analysis Attitude Level per User 7
8 Remove anonymous users, group by PostID Text Mining Words Tagging MPQA Corpus Positive words Negative words BoW Standard Named Entity Filter Word Frequency User Bins Word cloud for selected users
9 Slashdot Text Mining List of negative and positive words (MPQA Opinion Corpus) Tag positive and negative words Count words in posts Aggregate over users Negative + Positive User. Most positive user: dada21 (2838 positive / 1725 negative words) Most negative user: pnutz (43 positive / 109 negative words) positive users 7107 negative users Which Topics have positive users in common? Government People Law/s Money Market Parties
10 Network Creation User1 User2 User3 User4 User5 User6 10
11 Topic Graphs 11
12 Topic Graph: NASA 12
13 Hubs & Authorities Hubs = Follower Authorities = Leader Users with hub and authority weights and other features Filtering anonymous users and creating network Centrality index to define hub weight and authority weight 13
14 Hubs & Authorities dada21 Carl Bialik from the WSJ pnutz Tube Steak Doc Ruby 99BottlesOfBeerInMyF 14
15 Hubs, Authorities &Attitudes dada21 Carl Bialik from the WSJ Tube Steak WebHosting Guy Catbeller 99BottlesOfBeerInMyF Doc Ruby pnutz 15
16 What we have found... - The positive leaders - The neutral leaders - The negative leaders - The inactive users What identifies each group? How do I identify a new user? How do I handle each user? 16
17 User Classification Authority Score Histogram Hub Score Histogram How do I define leadership? 17
18 Attitude Level Histogram Defining thresholds on attitude might be easier 18
19 Why Clustering? - No a priori knowledge (not even on a subset of users) - Prediction and interpretation capabilities required k-means algorithm 19
20 Normalization (Authority score, Hub score) in [0,1] x [0,1] Attitude level in [-66, 1113] 20
21 Authority after Normalization Leadership is now a bit easier to obtain. 21
22 Hub Score after Normalization Also the follower condition is more spread out. 22
23 Attitude after Normalization Attitude is the only parameter that is now easier to identify. 23
24 Number of Clusters Users with a negative attitude are hard to catch! K=30: 10 clusters with more than 1000 users; 2 clusters with clear negative attitude (< 0.4) K=20: 5 clusters with more than 1000 users; 2 clusters with negative attitude (<0.4) K=10: 2 clusters with more than 5000 users and no cluster with a negative attitude anymore. 24
25 Re-sampling the Training Set k = 10 25
26 The k-means Clusters 26
27 Additional Discoveries There are only very few real leaders! Authority and hub scores identify active participants rather than leaders. Superfans can be found in cluster_3 Negative and (sigh!) active users are collected in cluster_1. Neutral users are usually inactive (cluster_2, cluster_7, and cluster_8) Positive users with different degrees of activity are scattered across the remaining clusters. 27
28 The k-means Clusters Neutral users Superfans Negative users Fans 28
29 The operational Workflow Pre-processing Cluster Extraction Assignment of new data 29
30 Full system to: Summary and Conclusions - Integrate text and network mining - Find meaningful clusters in terms of attitude and activity - Define appropriate actions for users in different clusters - Assign new data to existing clusters 30
31 Next Steps - Integrate topic information - Integrate user demographic and behavioural information - Discover [time series] patterns for early detection of negative users and superfans - Try other techniques, maybe even on manually segmented data, to discover new user segments 31
32 Where do I find more? Whitepaper: rosariasilipo@yahoo.com Complete Workflows + Data: - text mining - network mining - combined analysis (note the above 3 process huge data and require 16G memory) clustering Open Source Software: KNIME 32
Web Analysis in 4 Easy Steps. Rosaria Silipo, Bernd Wiswedel and Tobias Kötter
Web Analysis in 4 Easy Steps Rosaria Silipo, Bernd Wiswedel and Tobias Kötter KNIME Forum Analysis KNIME Forum Analysis Steps: 1. Get data into KNIME 2. Extract simple statistics (how many posts, response
More informationAPPLYING THE POWER OF AI TO YOUR VIDEO PRODUCTION STORAGE
APPLYING THE POWER OF AI TO YOUR VIDEO PRODUCTION STORAGE FINDING WHAT YOU NEED IN YOUR IN-HOUSE VIDEO STORAGE SECTION 1 You need ways to generate metadata for stored videos without time-consuming manual
More informationPowering Knowledge Discovery. Insights from big data with Linguamatics I2E
Powering Knowledge Discovery Insights from big data with Linguamatics I2E Gain actionable insights from unstructured data The world now generates an overwhelming amount of data, most of it written in natural
More informationAn Oracle White Paper October Oracle Social Cloud Platform Text Analytics
An Oracle White Paper October 2012 Oracle Social Cloud Platform Text Analytics Executive Overview Oracle s social cloud text analytics platform is able to process unstructured text-based conversations
More information7 Techniques for Data Dimensionality Reduction
7 Techniques for Data Dimensionality Reduction Rosaria Silipo KNIME.com The 2009 KDD Challenge Prediction Targets: Churn (contract renewals), Appetency (likelihood to buy specific product), Upselling (likelihood
More informationSentiment Web Mining Architecture - Shahriar Movafaghi
Available online at www.sciencedirect.com Procedia - Social and Behavioral Sciences 26 (2011) 191 197 COINs 2010 Sentiment Web Mining Architecture - Shahriar Movafaghi Shahria Movafaghi a, Jack Bullock
More informationSCALABLE KNOWLEDGE BASED AGGREGATION OF COLLECTIVE BEHAVIOR
SCALABLE KNOWLEDGE BASED AGGREGATION OF COLLECTIVE BEHAVIOR P.SHENBAGAVALLI M.E., Research Scholar, Assistant professor/cse MPNMJ Engineering college Sspshenba2@gmail.com J.SARAVANAKUMAR B.Tech(IT)., PG
More informationViewpoint Review & Analytics
The Viewpoint all-in-one e-discovery platform enables law firms, corporations and service providers to manage every phase of the e-discovery lifecycle with the power of a single product. The Viewpoint
More informationDATA MINING II - 1DL460. Spring 2014"
DATA MINING II - 1DL460 Spring 2014" A second course in data mining http://www.it.uu.se/edu/course/homepage/infoutv2/vt14 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,
More informationOverview of Web Mining Techniques and its Application towards Web
Overview of Web Mining Techniques and its Application towards Web *Prof.Pooja Mehta Abstract The World Wide Web (WWW) acts as an interactive and popular way to transfer information. Due to the enormous
More informationData Management Glossary
Data Management Glossary A Access path: The route through a system by which data is found, accessed and retrieved Agile methodology: An approach to software development which takes incremental, iterative
More informationGoogle Marketing Boot Camp 3 Days
Google Marketing Boot Camp 3 Days Course Overview If your business is online, you need to know how to successfully implement and analyze Google-based Internet marketing campaigns. There are a large number
More informationThe Top 10 New Features in KNIME 2.8. Rosaria Silipo KNIME.com AG, San Francisco
The Top 10 New Features in KNIME 2.8 Rosaria Silipo KNIME.com AG, San Francisco KNIME 2.8 KNIME 2.8 was out end of July 2013 Many New Features Documentation available at: http://tech.knime.org/whats-new-in-knime-28
More informationLIDER Survey. Overview. Number of participants: 24. Participant profile (organisation type, industry sector) Relevant use-cases
LIDER Survey Overview Participant profile (organisation type, industry sector) Relevant use-cases Discovering and extracting information Understanding opinion Content and data (Data Management) Monitoring
More informationUNIT-V WEB MINING. 3/18/2012 Prof. Asha Ambhaikar, RCET Bhilai.
UNIT-V WEB MINING 1 Mining the World-Wide Web 2 What is Web Mining? Discovering useful information from the World-Wide Web and its usage patterns. 3 Web search engines Index-based: search the Web, index
More informationGETTING STARTED WITH DATA MINING
GETTING STARTED WITH DATA MINING Nora Galambos, PhD Senior Data Scientist Office of Institutional Research, Planning & Effectiveness Stony Brook University AIR Forum 2017 Washington, D.C. 1 Using Data
More informationKNIME for the life sciences Cambridge Meetup
KNIME for the life sciences Cambridge Meetup Greg Landrum, Ph.D. KNIME.com AG 12 July 2016 What is KNIME? A bit of motivation: tool blending, data blending, documentation, automation, reproducibility More
More informationGraph Mining and Social Network Analysis
Graph Mining and Social Network Analysis Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References q Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
More informationInteractive Campaign Planning for Marketing Analysts
Interactive Campaign Planning for Marketing Analysts Fan Du University of Maryland College Park, MD, USA fan@cs.umd.edu Sana Malik Adobe Research San Jose, CA, USA sana.malik@adobe.com Eunyee Koh Adobe
More informationKara Greenfield, William Campbell, Joel Acevedo-Aviles
Kara Greenfield, William Campbell, Joel Acevedo-Aviles GraphEx 2014 8/21/2014 This work was sponsored by the Defense Advanced Research Projects Agency under Air Force Contract FA8721-05-C-0002. Opinions,
More informationD4 WHITEPAPER powered by people THREE METHODS OF EDISCOVERY DOCUMENT REVIEW COMPARED
D4 WHITEPAPER powered by people THREE METHODS OF EDISCOVERY DOCUMENT REVIEW COMPARED This whitepaper compares keyword search, concept-based review methods, and support vector-based review platforms; how
More informationMining Social Media Users Interest
Mining Social Media Users Interest Presenters: Heng Wang,Man Yuan April, 4 th, 2016 Agenda Introduction to Text Mining Tool & Dataset Data Pre-processing Text Mining on Twitter Summary & Future Improvement
More informationMicrosoft SharePoint Server 2013 Plan, Configure & Manage
Microsoft SharePoint Server 2013 Plan, Configure & Manage Course 20331-20332B 5 Days Instructor-led, Hands on Course Information This five day instructor-led course omits the overlap and redundancy that
More informationDeploying, Managing and Reusing R Models in an Enterprise Environment
Deploying, Managing and Reusing R Models in an Enterprise Environment Making Data Science Accessible to a Wider Audience Lou Bajuk-Yorgan, Sr. Director, Product Management Streaming and Advanced Analytics
More informationUSER GUIDE DASHBOARD OVERVIEW A STEP BY STEP GUIDE
USER GUIDE DASHBOARD OVERVIEW A STEP BY STEP GUIDE DASHBOARD LAYOUT Understanding the layout of your dashboard. This user guide discusses the layout and navigation of the dashboard after the setup process
More informationChuck Cartledge, PhD. 24 February 2018
Big Data: Data Wrangling Boot Camp R Sentiment Analysis Chuck Cartledge, PhD 24 February 2018 1/33 Table of contents (1 of 1) 1 Intro. 2 Preview Things that will be happening today How we ll get there
More informationAnalysis of Nokia Customer Tweets with SAS Enterprise Miner and SAS Sentiment Analysis Studio
Analysis of Nokia Customer Tweets with SAS Enterprise Miner and SAS Sentiment Analysis Studio Vaibhav Vanamala MS in Business Analytics, Oklahoma State University SAS and all other SAS Institute Inc. product
More information1. Inroduction to Data Mininig
1. Inroduction to Data Mininig 1.1 Introduction Universe of Data Information Technology has grown in various directions in the recent years. One natural evolutionary path has been the development of the
More informationIntroduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p.
Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. 6 What is Web Mining? p. 6 Summary of Chapters p. 8 How
More informationMarketing Automation Functional Evaluation Guide
Marketing Automation Functional Evaluation Guide Evaluating Marketing Automation Functionality Software Advice has analyzed the core functionality of the leading marketing automation systems and determined
More informationSAS. Contextual Analysis 13.2: User s Guide. SAS Documentation
SAS Contextual Analysis 13.2: User s Guide SAS Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2014. SAS Contextual Analysis 13.2: User's Guide. Cary,
More informationMarketing Automation
Marketing Automation Index 1. About TAMSAN 2. What is Marketing Automation? 3. About mautic (Overview and functions) 4. Demo - How to create scenarios About TAMSAN 1. About TAMSAN (1/5) About us TAMSAN
More informationContextual Search using Cognitive Discovery Capabilities
Contextual Search using Cognitive Discovery Capabilities In this exercise, you will work with a sample application that uses the Watson Discovery service API s for cognitive search use cases. Discovery
More informationBuilding Search Applications
Building Search Applications Lucene, LingPipe, and Gate Manu Konchady Mustru Publishing, Oakton, Virginia. Contents Preface ix 1 Information Overload 1 1.1 Information Sources 3 1.2 Information Management
More informationUSER GUIDE DESIGN A STEP BY STEP GUIDE
USER GUIDE DESIGN A STEP BY STEP GUIDE UNDERSTANDING THE NEW DESIGN TAB Users with Design privileges choose how your data will display within your dashboard visually. Under DASHBOARD DESIGN, you can change
More informationTEXT ANALYTICS USING AZURE COGNITIVE SERVICES
EMAIL TEXT ANALYTICS USING AZURE COGNITIVE SERVICES Feature that provides Organizations Language Translation and Sentiment Score for Email Text Messages using Azure s Cognitive Services. MICROSOFT LABS
More informationMicrosoft Core Solutions of Microsoft SharePoint Server 2013
1800 ULEARN (853 276) www.ddls.com.au Microsoft 20331 - Core Solutions of Microsoft SharePoint Server 2013 Length 5 days Price $4290.00 (inc GST) Version B Overview This course will provide you with the
More informationSAMPLE 2 This is a sample copy of the book From Words to Wisdom - An Introduction to Text Mining with KNIME
2 Copyright 2018 by KNIME Press All Rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval
More informationNuix ediscovery Specialist
Nuix ediscovery Specialist Nuix ediscovery Specialist ADVANCE TWO-DAY INSTRUCTOR-LED COURSE Nuix ediscovery Specialist training is a two-day course that will work through the complete ediscovery workflow,
More informationPart I: Data Mining Foundations
Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web and the Internet 2 1.3. Web Data Mining 4 1.3.1. What is Data Mining? 6 1.3.2. What is Web Mining?
More informationOverview. Data-mining. Commercial & Scientific Applications. Ongoing Research Activities. From Research to Technology Transfer
Data Mining George Karypis Department of Computer Science Digital Technology Center University of Minnesota, Minneapolis, USA. http://www.cs.umn.edu/~karypis karypis@cs.umn.edu Overview Data-mining What
More informationQualitative Data Analysis Software. A workshop for staff & students School of Psychology Makerere University
Qualitative Data Analysis Software A workshop for staff & students School of Psychology Makerere University (PhD) January 27, 2016 Outline for the workshop CAQDAS NVivo Overview Practice 2 CAQDAS Before
More informationUsing the Force of Python and SAS Viya on Star Wars Fan Posts
SESUG Paper BB-170-2017 Using the Force of Python and SAS Viya on Star Wars Fan Posts Grace Heyne, Zencos Consulting, LLC ABSTRACT The wealth of information available on the Internet includes useful and
More informationHortonworks DataPlane Service
Data Steward Studio Administration () docs.hortonworks.com : Data Steward Studio Administration Copyright 2016-2017 Hortonworks, Inc. All rights reserved. Please visit the Hortonworks Data Platform page
More informationElection Analysis and Prediction Using Big Data Analytics
Election Analysis and Prediction Using Big Data Analytics Omkar Sawant, Chintaman Taral, Roopak Garbhe Students, Department Of Information Technology Vidyalankar Institute of Technology, Mumbai, India
More informationINTRODUCTION... 2 FEATURES OF DARWIN... 4 SPECIAL FEATURES OF DARWIN LATEST FEATURES OF DARWIN STRENGTHS & LIMITATIONS OF DARWIN...
INTRODUCTION... 2 WHAT IS DATA MINING?... 2 HOW TO ACHIEVE DATA MINING... 2 THE ROLE OF DARWIN... 3 FEATURES OF DARWIN... 4 USER FRIENDLY... 4 SCALABILITY... 6 VISUALIZATION... 8 FUNCTIONALITY... 10 Data
More informationTaxonomy Tools: Collaboration, Creation & Integration. Dow Jones & Company
Taxonomy Tools: Collaboration, Creation & Integration Dave Clarke Global Taxonomy Director dave.clarke@dowjones.com Dow Jones & Company Introduction Software Tools for Taxonomy 1. Collaboration 2. Creation
More informationFoundations of Business Intelligence: Databases and Information Management
Foundations of Business Intelligence: Databases and Information Management TOPIC 1: Foundations of Business Intelligence: Databases and Information Management TOPIC 1: Foundations of Business Intelligence:
More informationInternational Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May Dr.Zakea Il-Agure and Mr.Hicham Noureddine Itani
LINK MINING PROCESS Dr.Zakea Il-Agure and Mr.Hicham Noureddine Itani Higher Colleges of Technology, United Arab Emirates ABSTRACT Many data mining and knowledge discovery methodologies and process models
More informationSemantic Web Company. PoolParty - Server. PoolParty - Technical White Paper.
Semantic Web Company PoolParty - Server PoolParty - Technical White Paper http://www.poolparty.biz Table of Contents Introduction... 3 PoolParty Technical Overview... 3 PoolParty Components Overview...
More informationExploratory Analysis: Clustering
Exploratory Analysis: Clustering (some material taken or adapted from slides by Hinrich Schutze) Heejun Kim June 26, 2018 Clustering objective Grouping documents or instances into subsets or clusters Documents
More informationINTRODUCTION TO BIG DATA, DATA MINING, AND MACHINE LEARNING
CS 7265 BIG DATA ANALYTICS INTRODUCTION TO BIG DATA, DATA MINING, AND MACHINE LEARNING * Some contents are adapted from Dr. Hung Huang and Dr. Chengkai Li at UT Arlington Mingon Kang, PhD Computer Science,
More informationData Mining: Approach Towards The Accuracy Using Teradata!
Data Mining: Approach Towards The Accuracy Using Teradata! Shubhangi Pharande Department of MCA NBNSSOCS,Sinhgad Institute Simantini Nalawade Department of MCA NBNSSOCS,Sinhgad Institute Ajay Nalawade
More informationInformation Management Fundamentals by Dave Wells
Information Management Fundamentals by Dave Wells All rights reserved. Reproduction in whole or part prohibited except by written permission. Product and company names mentioned herein may be trademarks
More informationPrivacy Challenges in Big Data and Industry 4.0
Privacy Challenges in Big Data and Industry 4.0 Jiannong Cao Internet & Mobile Computing Lab Department of Computing Hong Kong Polytechnic University Email: csjcao@comp.polyu.edu.hk http://www.comp.polyu.edu.hk/~csjcao/
More informationUSC Viterbi School of Engineering
Introduction to Computational Thinking and Data Science USC Viterbi School of Engineering http://www.datascience4all.org Term: Fall 2016 Time: Tues- Thur 10am- 11:50am Location: Allan Hancock Foundation
More informationCase Study: Social Network Analysis. Part II
Case Study: Social Network Analysis Part II https://sites.google.com/site/kdd2017iot/ Outline IoT Fundamentals and IoT Stream Mining Algorithms Predictive Learning Descriptive Learning Frequent Pattern
More information2 The IBM Data Governance Unified Process
2 The IBM Data Governance Unified Process The benefits of a commitment to a comprehensive enterprise Data Governance initiative are many and varied, and so are the challenges to achieving strong Data Governance.
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Murhaf Fares & Stephan Oepen Language Technology Group (LTG) September 27, 2017 Today 2 Recap Evaluation of classifiers Unsupervised
More informationAn overview of Graph Categories and Graph Primitives
An overview of Graph Categories and Graph Primitives Dino Ienco (dino.ienco@irstea.fr) https://sites.google.com/site/dinoienco/ Topics I m interested in: Graph Database and Graph Data Mining Social Network
More informationManagement Information Systems Review Questions. Chapter 6 Foundations of Business Intelligence: Databases and Information Management
Management Information Systems Review Questions Chapter 6 Foundations of Business Intelligence: Databases and Information Management 1) The traditional file environment does not typically have a problem
More informationDIGIT.B4 Big Data PoC
DIGIT.B4 Big Data PoC DIGIT 01 Social Media D02.01 PoC Requirements Table of contents 1 Introduction... 5 1.1 Context... 5 1.2 Objective... 5 2 Data SOURCES... 6 2.1 Data sources... 6 2.2 Data fields...
More informationDATA MINING AND DATABASE TECHNOLOGY (WEB MINING, TEXT MINING, SENTIMENTAL ANALYSIS FOR SOCIAL MEDIA, TOOLS, TECHNIQUES, METHODS,
1 Data Mining and Database Technology (Web Mining, Text Mining, Sentimental Analysis for social media, tools, techniques, methods, applications etc.) 2 Abstract This paper discusses significance of Database
More informationRELEASE NOTES. Overview: Introducing ForeSee CX Suite
RELEASE NOTES Overview: Introducing ForeSee CX Suite FALL 2016 CONTENTS Overview of ForeSee CX Suite...4 First Release of CX Suite... 4 The Login Page... 4 Dashboards... 4 Surveys... 5 Cases... 5 Text
More informationD DAVID PUBLISHING. Big Data; Definition and Challenges. 1. Introduction. Shirin Abbasi
Journal of Energy and Power Engineering 10 (2016) 405-410 doi: 10.17265/1934-8975/2016.07.004 D DAVID PUBLISHING Shirin Abbasi Computer Department, Islamic Azad University-Tehran Center Branch, Tehran
More informationTDWI strives to provide course books that are content-rich and that serve as useful reference documents after a class has ended.
Previews of TDWI course books are provided as an opportunity to see the quality of our material and help you to select the courses that best fit your needs. The previews can not be printed. TDWI strives
More informationSpotfire Data Science with Hadoop Using Spotfire Data Science to Operationalize Data Science in the Age of Big Data
Spotfire Data Science with Hadoop Using Spotfire Data Science to Operationalize Data Science in the Age of Big Data THE RISE OF BIG DATA BIG DATA: A REVOLUTION IN ACCESS Large-scale data sets are nothing
More informationdata-based banking customer analytics
icare: A framework for big data-based banking customer analytics Authors: N.Sun, J.G. Morris, J. Xu, X.Zhu, M. Xie Presented By: Hardik Sahi Overview 1. 2. 3. 4. 5. 6. Why Big Data? Traditional versus
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Erik Velldal & Stephan Oepen Language Technology Group (LTG) September 23, 2015 Agenda Last week Supervised vs unsupervised learning.
More informationText Mining: A Burgeoning technology for knowledge extraction
Text Mining: A Burgeoning technology for knowledge extraction 1 Anshika Singh, 2 Dr. Udayan Ghosh 1 HCL Technologies Ltd., Noida, 2 University School of Information &Communication Technology, Dwarka, Delhi.
More informationNon-trivial extraction of implicit, previously unknown and potentially useful information from data
CS 795/895 Applied Visual Analytics Spring 2013 Data Mining Dr. Michele C. Weigle http://www.cs.odu.edu/~mweigle/cs795-s13/ What is Data Mining? Many Definitions Non-trivial extraction of implicit, previously
More informationCIRGDISCO at RepLab2012 Filtering Task: A Two-Pass Approach for Company Name Disambiguation in Tweets
CIRGDISCO at RepLab2012 Filtering Task: A Two-Pass Approach for Company Name Disambiguation in Tweets Arjumand Younus 1,2, Colm O Riordan 1, and Gabriella Pasi 2 1 Computational Intelligence Research Group,
More informationEvaluating the Usefulness of Sentiment Information for Focused Crawlers
Evaluating the Usefulness of Sentiment Information for Focused Crawlers Tianjun Fu 1, Ahmed Abbasi 2, Daniel Zeng 1, Hsinchun Chen 1 University of Arizona 1, University of Wisconsin-Milwaukee 2 futj@email.arizona.edu,
More informationMining Web Data. Lijun Zhang
Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems
More informationSEO and Monetizing The Content. Digital 2011 March 30 th Thinking on a different level
SEO and Monetizing The Content Digital 2011 March 30 th 2011 Getting Found and Making the Most of It 1. Researching target Audience (Keywords) 2. On-Page Optimisation (Content) 3. Titles and Meta Tags
More informationData Mining. Introduction. Piotr Paszek. (Piotr Paszek) Data Mining DM KDD 1 / 44
Data Mining Piotr Paszek piotr.paszek@us.edu.pl Introduction (Piotr Paszek) Data Mining DM KDD 1 / 44 Plan of the lecture 1 Data Mining (DM) 2 Knowledge Discovery in Databases (KDD) 3 CRISP-DM 4 DM software
More informationTime: 3 hours. Full Marks: 70. The figures in the margin indicate full marks. Answers from all the Groups as directed. Group A.
COPYRIGHT RESERVED End Sem (V) MCA (XXVIII) 2017 Time: 3 hours Full Marks: 70 Candidates are required to give their answers in their own words as far as practicable. The figures in the margin indicate
More informationOracle9i Data Mining. An Oracle White Paper December 2001
Oracle9i Data Mining An Oracle White Paper December 2001 Oracle9i Data Mining Benefits and Uses of Data Mining... 2 What Is Data Mining?... 3 Data Mining Concepts... 4 Using the Past to Predict the Future...
More informationAnalysis of Tweets: Donald Trump and sexual harassment allegations
Analysis of Tweets: Donald Trump and sexual harassment allegations MAIN CONCEPT Twitter historical database was used to search for suitable tweets. Search term of Trump and harassment was consequently
More informationOracle9i Data Mining. Data Sheet August 2002
Oracle9i Data Mining Data Sheet August 2002 Oracle9i Data Mining enables companies to build integrated business intelligence applications. Using data mining functionality embedded in the Oracle9i Database,
More informationJohn Biancamano Inbound Digital LLC InboundDigital.net
John Biancamano Inbound Digital LLC 609.865.7994 InboundDigital.net About Me Owner of Inbound Digital, LLC digital marketing consulting and training: websites, SEO, advertising, and social media. Senior
More informationNLP Final Project Fall 2015, Due Friday, December 18
NLP Final Project Fall 2015, Due Friday, December 18 For the final project, everyone is required to do some sentiment classification and then choose one of the other three types of projects: annotation,
More information*ANSWERS * **********************************
CS/183/17/SS07 UNIVERSITY OF SURREY BSc Programmes in Computing Level 1 Examination CS183: Systems Analysis and Design Time allowed: 2 hours Spring Semester 2007 Answer ALL questions in Section A and TWO
More informationA data-driven framework for archiving and exploring social media data
A data-driven framework for archiving and exploring social media data Qunying Huang and Chen Xu Yongqi An, 20599957 Oct 18, 2016 Introduction Social media applications are widely deployed in various platforms
More informationIBM Watson Application Developer Workshop. Watson Knowledge Studio: Building a Machine-learning Annotator with Watson Knowledge Studio.
IBM Watson Application Developer Workshop Lab02 Watson Knowledge Studio: Building a Machine-learning Annotator with Watson Knowledge Studio January 2017 Duration: 60 minutes Prepared by Víctor L. Fandiño
More informationFeature selection. LING 572 Fei Xia
Feature selection LING 572 Fei Xia 1 Creating attribute-value table x 1 x 2 f 1 f 2 f K y Choose features: Define feature templates Instantiate the feature templates Dimensionality reduction: feature selection
More informationSemantic Systems & Visual Tools to Analyze Climate Change Communication
1 Semantic Systems & Visual Tools to Analyze Climate Change Communication Prof Arno Scharl scharl@weblyzard.com Barcelona Supercomputing Center, 13 May 2016 2 Overview News and social media channels represent
More informationVisualization and text mining of patent and non-patent data
of patent and non-patent data Anton Heijs Information Solutions Delft, The Netherlands http://www.treparel.com/ ICIC conference, Nice, France, 2008 Outline Introduction Applications on patent and non-patent
More informationGale Digital Scholar Lab Getting Started Walkthrough Guide
Getting Started Logging In Your library or institution will provide you with your login link. You will have the option to sign in with a Google or Microsoft Account, this is so you have a personal account
More informationempythy Documentation
empythy Documentation Release 0.9.1 Preston Parry August 29, 2016 Contents 1 Installation 3 2 Core Functionality 5 3 Basic API Documentation 7 4 Training on your own corpus 9 i ii empythy Documentation,
More informationSAS Factory Miner 14.2: User s Guide
SAS Factory Miner 14.2: User s Guide SAS Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2016. SAS Factory Miner 14.2: User s Guide. Cary, NC: SAS Institute
More informationCOCKPIT FP Citizens Collaboration and Co-Creation in Public Service Delivery. Deliverable D Opinion Mining Tools 1st version
COCKPIT FP7-248222 Citizens Collaboration and Co-Creation in Public Service Delivery Deliverable D2.1.1 Opinion Mining Tools 1st version Editor(s): Responsible Partner: Kostas Giannakakis ATC, INTRASOFT
More informationTracking 101 DISCOVER HOW TRACKING HELPS YOU UNDERSTAND AND TRULY ENGAGE YOUR AUDIENCES, TURNING INTO RESULTS
Email Tracking 101 DISCOVER HOW EMAIL TRACKING HELPS YOU UNDERSTAND AND TRULY ENGAGE YOUR AUDIENCES, TURNING EMAIL INTO RESULTS SUMMARY 2 INTRODUCTION TO EMAIL TRACKING 3 WHAT IS EMAIL TRACKING? 4 WHAT
More informationADVANCED ANALYTICS USING SAS ENTERPRISE MINER RENS FEENSTRA
INSIGHTS@SAS: ADVANCED ANALYTICS USING SAS ENTERPRISE MINER RENS FEENSTRA AGENDA 09.00 09.15 Intro 09.15 10.30 Analytics using SAS Enterprise Guide Ellen Lokollo 10.45 12.00 Advanced Analytics using SAS
More informationData Mining. Ryan Benton Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette, La., USA.
Data Mining Ryan Benton Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette, La., USA January 13, 2011 Important Note! This presentation was obtained from Dr. Vijay Raghavan
More informationCOURSE BROCHURE. ITIL - Intermediate Service Transition. Training & Certification
COURSE BROCHURE ITIL - Intermediate Service Transition. Training & Certification What is ITIL ST? The intermediate level of ITIL offers a role based hands-on experience and in-depth coverage of the contents.
More informationSocial Business Intelligence in Action
Social Business Intelligence in ction Matteo Francia, nrico Gallinucci, Matteo Golfarelli, Stefano Rizzi DISI University of Bologna, Italy Introduction Several Social-Media Monitoring tools are available
More informationAutomated Classification. Lars Marius Garshol Topic Maps
Automated Classification Lars Marius Garshol Topic Maps 2007 2007-03-21 Automated classification What is it? Why do it? 2 What is automated classification? Create parts of a topic map
More informationData Mining Concepts. Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech
http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Data Mining Concepts Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech Partly based on
More informationAcquiring, Exploring and Preparing the Data
Technical Appendix Catch the Pink Flamingo Analysis Produced by: Prabhat Tripathi Acquiring, Exploring and Preparing the Data Data Exploration Data Set Overview The table below lists each of the files
More information