Social Media Intelligence

Text, Network Mining and Predictive

Analytics Combined

Phil Winters

Tobias Kötter

www.knime.com

Social Media Analysis Water Water Everywhere, and not a drop to drink

Approaches and Challenges:

Cloud-based Approach: No Access to Data

In-House Dashboard: No Analytics

In-House Text Mining: Sentiment but no relevance

In-House Network Mining: Relevance but no Sentiment

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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

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Social Media Intelligence:

Major European Telco

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Our Goal in Social Media Analysis

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Text Mining for Sentiment

Drill Down on special cases

Network Mining for Relevance

Analytics for Prediction

Case Study Example: Slashdot Data

“News for Nerds, Stuff that Matters“

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Basic Facts:

• 24532 users

• 491 threads with

15 – 843 responses

from 12 – 507 users

• 113505 posts

(text mining on posts)

• 60 main topics

Text Mining Remove anonymous users,

group by PostID Words Tagging

Positive words

Negative words

MPQA

Corpus

BoW

Sta

nd

ard

Nam

ed

En

tity

Filt

er

Word

Fre

qu

en

cy

User Bins

Word cloud for selected users 7

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)

16016 positive users

7107 negative users

Which Topics have positive users in common ?

– Government

– People

– Law/s

– Money

– Market

– Parties

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Slashdot – Text Mining

Most negative post:

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Installation

Network Mining Feature:

labs.knime.org

Documentation:

http://tech.knime.org/network-mining

Data Structure

Supported networks:

– (un)directed

– (un)weighted

– hypergraph

– k-partite

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Nodes

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13 planned

Internal based on Jung 2.0.1

Cytoscape

visone

Gephi

Visualization

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Network Creation

User1

User2 User3

User6

User4 User5

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Network Creation

Networking Mining the Slashdot Data

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Topic Graphs

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Topic Graphs

Topic Graphs

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NASA

Sci-Fi

Hubs & Authorities

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• Hubs = Follower

• Authorities = Leader

Filtering anonymous users and creating network Centrality index to

define hub weight

and authority weight

Users with hub and

authority weights and

other features

Hubs & Authorities

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dada21

Doc Ruby

Carl Bialik

pNutz

Tube Steak

Combining Text and Network Mining

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Network Analysis

Text Analysis

Hub and Authority Score

per User

Attitude Level per User

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Carl Bialik

dada21

Doc Ruby

WebH

osting G

uy

pNutz

Tube Steak

Catbeller

Hubs, Authorities &Attitudes

from the WSJ

What we have found ...

- The positive leaders

- The neutral leaders

- The negative leaders

- The inactive users

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What identifies each group?

How do I identify a new user?

How do I handle each user?

The k-Means Clusters

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Superfans

Negative

users

Neutral

users

Fans

The operational Workflow

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Pre-processing Cluster Extraction

Assignment of new data

Lessons Learned Data Manipulation is the key…. The decision science flows from that

Sentiment analysis is all about the Corpus !

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Network Analysis

Sentiment Analysis

Capturing the data Options Available: From fee-paying to open source !

NOTE

Examples, workflows (ie: the complete

programs) as well as white papers are

available for download on:

www.knime.com

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Copyright © 2013 by KNIME.com AG All Rights Reserved - Confidential

Mark Your Calendars:

KNIME’s 7th User Group Meeting

19.-20. February 2014 Zurich, Switzerland

www.KNIME.com

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