This document describes using KNIME analytics platform to analyze social media data from a Slashdot forum to understand user sentiment and identify influencers. Key steps included performing text analytics like sentiment analysis and topic modeling on posts and comments. Network analysis was also done to identify influential users based on interactions. Integrating text and network results identified the most positive, negative, and neutral influential users to help marketing efforts. Resources for learning more about KNIME analytics are also provided.

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Customer Intelligence on Social
Media
Kilian Thiel
Tobias Koetter
Phil Winters
Rosaria Silipo
KNIME.com AG

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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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The KNIME Analytics Platform
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Statistics
Data Mining
Machine Learning
Web Analytics
Text Mining
Network Analysis
Social Media Analysis
WEKA
R
JFreeChart
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
via BIRT
PMML
XML
Databases
Excel, Flat, etc.
Hive etc.
Spark
Text, Doc, Image
Industry Specific
Community / 3rd
Over 1000 native and embedded nodes included:
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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

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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

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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

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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

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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.

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Text Analytics: Workflow
Document type
is required
Loading MPQA
Stanford dictionary for
sentiment attribute
Sumoffrequencies
ofpositiveand
negativewordsper
post/comment
SumofSumof
frequenciesofpositive
andnegativewordsper
user
ScatterPlots
andTagClouds
ReadData

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Text Analytics: Results
Most negative
user pNutz
Most positive
and most
talkative user
dada21

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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?

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Network Mining: Options
• User Interaction Graph
• Influencers vs. Followers
• User Network Investigation

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Network Mining: Workflow
Read Data
Create Empty
Network
Create
Network
Content
Extract
largest
sub-graph
Centrality Index for
authority score
Scatter Plots

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Network Mining: Results
Dada21
Carl Bialik from the WSJ
Doc Ruby

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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?

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Text Analytics and Network Mining: Workflow
Read Data
Network Mining
Sentiment Analysis
Joiner
Scatter Plots

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Text Analytics and Network Mining: Results
pNutz
Carl Bialik
dada21
Doc Ruby
99BottlesOfBeerInMyF
WebHosting Guy
Tube Steak
Catbeller
from the WSJ

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Text Analytics and Network Mining: Results

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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.

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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_whit
e_paper.pdf

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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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