Knime customer intelligence on social media odsc london

Copyright © 2015 KNIME.com AG

Customer Intelligence on Social Media

Rosaria Silipo

KNIME.com AG

Copyright © 2014 KNIME.com AG 2


The KNIME Platform: Open for Innovation

Powerful: Legacy Future Tools

Collaborative: Scientists Analysts

Integrative: Legacy Future Data

Transparent: Existing Future Expertise

Agile: Internal External Wisdom

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KNIME Analytics Platform

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NODES perform tasks on data

Nodes are combined to createWORKFLOWS

Status

Visual KNIME Workflows

Inputs Outputs

Not Configured

Idle

Executed

Error


Statistics

Data Mining

Machine Learning

Web Analytics

Text Mining

Network Analysis

Social Media Analysis

WEKA

R

Community / 3rd

MySQL, Oracle, etc.

SAS, SPSS, etc.

Excel, Flat, etc.

Hive etc.

XML, PMML

Text, Doc, Image

Web Crawlers

Industry Specific

Community / 3rd

ETL

Row,

Column

Matrix

Text, Image

Time Series

Java

Python

Community / 3rd

Includes over 1000 native and embedded nodes

via BIRT

PMML

XML

Databases

Excel, Flat, etc.

Hive etc.

Text, Doc, Image

Industry Specific

Community / 3rd

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Who’s Using KNIME?

> 2,000,000 Downloads annually (in 2015)

> 70,000 Individuals using KNIME

> 5,000 Organizations using KNIME

> 700 Customers paying for KNIME

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Top in User Satisfaction

2012 & 2013 Rexer Analytics Survey

Users who know KNIME love it!

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Open Source

Overall


KNIME Analytics Platform: Try it Now!

1. Download fromwww.knime.com

2. Browse the KNIMELearning Hub atwww.knime.com/learning-hub

3. Check out KNIME Press“KNIME Beginner’s Guide”

4. Become active on our Forum!

http://www.knime.com/

http://www.knime.com/learning-hub



The Problem

A major European Telco

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• Can you tell us what people say about our new product?

• Can you tell us who is supporting the product and who trashing it?

• Of those, can you tell us who is an influencer?

Its Forum Site


The Data

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• The Data Set unfortunately cannot be shared

• Slashdot Forum Data are!

• Slashdot was a public forum built in 1997 and hosting a number of discussions: from software to philosophy, from science fiction to politics.

• Politics was the biggest discussion group

• So, politics is what we analyzed to find out:– What users were thinking about a political issue

– Who was pro and who was con

– Who was an influencer



The Politics Group in the Slashdot DataSet

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• 24 000 non anonymous users

• 496 posts

• 140 000 comments

• Most posts have around 200 comments



Text Analytics: Options

• Tag (Word) Clouds

• Topic Detection

• Topic Shift

• Sentiment Analysis

I find PRODUCT X to be very good and useful,but it is a bit too expensive.


Text Analytics: WorkflowDocument type

is required

Loading MPQA Stanford dictionary for

sentiment attribute

Sum

of

freq

uen

cies

o

f p

osi

tive

an

d

neg

ativ

e w

ord

s p

er

po

st/c

om

men

t

Sum

of

Sum

of

freq

uen

cies

of

po

siti

ve

and

neg

ativ

e w

ord

s p

er

use

r

Scatter Plo

ts an

d Tag C

lou

ds

Rea

d D

ata


Text Analytics: Results

Most negative user pNutz

Most positive and most

talkative user dada21


Text Analytics: Open Questions

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• Is dada21 an influencer?

• Is pNutz an influencer?

• Shall we take marketing actions about any of them?



Network Mining: Options

• User Interaction Graph

• Influencers vs. Followers

• User Network Investigation


Network Mining: Workflow

Read Data

Create Empty Network

Create Network Content

Extract largest

sub-graph

Centrality Index for authority score

Scatter Plots


Network Mining: Results

Dada21

Carl Bialik from the WSJ

Doc Ruby


Network Mining: Open Questions

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• Is Carl Bialik from WSJ positive or negative about the topic?

• Is dada21 positive or negative about the topic?

• What shall we do marketing-wise with non-influencers such as doc Ruby?



Text Analytics and Network Mining: Workflow

Read Data

Network Mining

Sentiment Analysis

Joiner

Scatter Plots


Text Analytics and Network Mining: Results

pNutz

Carl Bialik

dada21

Doc Ruby

99BottlesOfBeerInMyF

WebHosting Guy

Tube Steak

Catbeller

from the WSJ


Text Analytics and Network Mining: Results



Conclusions

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• Is Carl Bialik from WSJ is an influencer and … neutral.

• Most influencers are actually neutral.

• Worth it keep informed

• dada21 is talking positively about each topic. Worth it to pamper him/her.

• Of the negative talking users, pNutz though obnoxious, is not the main worry. Catbeller is.


Where can I find all this?

White paper, Workflows, and Data is available on the KNIME web site:

http://www.knime.com/white-papers (section Social Media)

https://www.knime.org/files/knime_social_media_white_paper.pdf

http://www.knime.com/white-papers

https://www.knime.org/files/knime_social_media_white_paper.pdf


Resources

• KNIME (www.knime.org)• BLOG for news, tips and tricks(www.knime.org/blog)

• FORUM for questions and answers (tech.knime.org/forum)

• EXAMPLE SERVER for example workflows

• LEARNING HUB (www.knime.org/learning-hub)

• KNIME TV channel on

• KNIME on @KNIME

• KNIME on

https://www.facebook.com/KNIMEanalytics

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http://www.knime.org/

http://www.knime.org/blog

http://tech.knime.org/forum

http://www.knime.org/learning-hub


Thank You

Free Copy of KNIME Beginner’s Luck Book at KNIME Press https://www.knime.org/knimepress

Promotion Code:

ODSCLondonMeetup

https://www.knime.org/knimepress

Knime customer intelligence on social media odsc london

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