7,814 Enterprise Applications of Text Intelligence
Autumn Term ‘15 – Prof. Dr. Andrea Back, IWI-HSG

Simple
applications
that you
already know

7
Google Ngram Viewer
Religion vs. Science & Freedom vs. Justice
With Google‘s
new tool
Ngram
Viewer, you
can visualize
the rise and
fall of
particular
keywords
across 5
million books
and 500
years.

8
Google Ngram Viewer
See how big cocaine was in Victorian times

Many text
analytics users
don’t understand
that they’re
doing text
analytics, nor do
they need to.
(Alta Plana
2014)

10
Definition and State of Practice
Text analytics, applied to social, online, and
enterprise data, aims to extract useful information
and create usable insights for business, personal,
government, and research ends.
While not every analytical application directly
involves text, every task – including analyses of
“machine data” and transactional records – may be
enriched by the inclusion of text-sourced
information.
There is no single or typical text analytics user,
application, technology, or solution.
Users and uses vary by industry, business function,
information source, and goal.
Source: Grimes, 2014

We focus on Enterprise Applications
New reportings enable fast detection of issues and trends
11
0
2000
4000
6000
8000
10000
12000
#Messages
Senderreihenfolge
Bildstörung
Internetgeschwindigkeit
Internetausfall
Router
Issue Issue
Source: Del Ponte et al., 2015
Swisscom Call Center

ABB Content Health Panel
Some fact about ABB
13
~150,000
employees
Present in
countries
+100
Formed in
1988
merger of Swiss (BBC, 1891)
and Swedish (ASEA, 1883)
engineering companies
in revenue (2013)
$42b

ABB Content Health Panel
Biggest challenge for improving content effectiveness
14
 Directives & Guidelines do not drive change
1 million pages
250,000 products
3,500 editors
27 languages
20 web applications60 country web sites
How to drive scalable change?

Our Schedule
Date & Time Room Unit
14.09.2015 14:15 - 16:00 23-203 1 – Kick-off and Introduction
21.09.2015 14:15 - 16:00 01-111 2 – Webinar Topic Analyst
28.09.2015 14:15 - 16:00 23-203 3 – Overview of Use Cases and
Technical Basics
Guest lecture S. Paulutt, CID
12.10.2015 14:15 - 16:00
alternative 16:15 – 18:00
23-203 4 – Technical Basics and
Terminology
19.10.2015 14:15 - 16:00 23-203 Written exam
16

Our Schedule
Date & Time Room Unit
26.10.2015 14:15 - 16:00 01-307 5 – Guest lecture T. Lehr: Social
Media Monitoring
09.11.2015 14:15 - 18:00 EXT 6 – Google Zuerich (Google@Zue
– Office Tour – Advanced Search
– Industry Analytics)
16.11.2015 14:15 - 16:00 EXT 7 – Swisscom Innovation Zuerich
23.11.2015 14:15 - 18:00 EXT 8 – AXA Winterthur, Winterthur
30.11.2015 14:15 - 18:00 EXT 9 – SIKA, Urdorf
07.12.2015 14:15 - 18:00 23-203 10a – Guest lecture Dr. Scholer,
Audi
10b – From TA-Idea to Adoption
10c – Guest lecture Chr. Smiela,
Swiss Re L&H
10d – Course wrap up and final
steps
17

Themes of the course
End-users and Line-of-Business-People as target groups
18
Persons Documents
Software
Didactical methodology
Motivation
Technology Basics
Use Cases for Text
Analytics & Benefits for
Business
Software Tools
Application of
Knowledge in Case
Studies

Introduction of
course
participants

Guest Lecturers and Host Companies
22
Tobias Lehr,
ex Goldbach Int.
26.10.
30.11.23.11.
Dr. Florian Hamel,
Axa Winterthur
Christian Frey,
SikaP. Warnking
Stephanie
Paulutt Christoph
Smiela,
Swiss Re

Your personal grade results from
Total 100 points
24
40 individual points
Written examination
60 group-points
30 points Groupwork 1
30 points Groupwork 2
(all group members given the same
grades)
5
bonus
points

25
Lectures and
obligatory readings
See reading list
Evaluation of Learning Outcome
Comprehension of content
Communication and presentation of results
What earns you points
Exam (40 points)
Learning goals:
 Build expertise required on end-user side for competent use
 Understand the use cases for text analytics
 Learn the basics about technology
 Being able to administer and support text analytics products as
line of business user

What earns you points
2 x Groupwork (60 points)
26
Excursions and Case Studies
Evaluation of Learning Outcome
Comprehension of content
Application of what you have learned on real cases
Creativity and self-initiative
Communication and presentation of results
Learning goals:
 Consolidate your theoretical knowledge through application on
real cases
 Gain practical insight, get to know experts, and learn how they
solve business problems using text analytics

Process and
due date for
assignment to
- 1 team and
- 2 excursions
that are credit-
relevant for
each team
to be announced via
StudyNet

Planning your time (3 credits – 90 hours)
Our estimate
Task Estimated hrs Actual hrs
1 – Kick-off and Introduction 7
2 – Webinar Topic Analyst 5
3 – Overview of Use Cases and Technical
Basics
including reading Assignment
8
4 – Technical Basics and Terminology
5 – Guest Lecture Social Media Monitoring
6
Exam 8
28

Planning your time (3 credits – 90 hours)
Our estimate
Task Estimated hrs Actual hrs
6/7/8/9 – Enterprise Application Visit (I. of 4) 13
6/7/8/9 – Enterprise Application Visit (II. of 4) 13
10 – Guest lecture Audi AG, From TA-Idea to
Adoption, Guest lecture SwissRe, Course
wrap up and final steps
6
Groupwork completion and finalizing excursion
reports
24
29

Readings
– Obligatory
– Recommended

What to read and where to find it
31
 Obligatory
 Gartner. (2014). Technology Overview for Text Analytics.
 Gartner. (2012). Who‘s Who In Text Analytics, 1-5.
 Mapegy. (2013). Solar Technology: Asia‘s Innovations are Pushing Into the
Fast Lane.
 Recommended
 Gartner. (2015). Four Data Preparation Challenges for Text Analytics.
 Sack, H. (2014). Knowledge Engineering with Semantic Web Technologies.
The texts can be found on StudyNet.

33
There exists a significant shortage of text analytics talent within
both IT departments and business units.

Many business analytics leaders do not realize that certain
kinds of business problems can be resolved by text analytics.

35
Organizations’ lack of focus on nontraditional and unstructured
data sources as valuable resources hinders enterprise wide text
analytics adoption and therefore remains a significant blind spot
for most enterprises.

36
Text Analytics is expected to go into the stage of Slope of
Enlightenment in the coming few years because it will become
more prevalent and because of the opportunities to be realized.

Gartner Analysts Found (2014)
Fragmented Provider Market: Categories and examples
 Small start-ups
 Established technology and solution companies
 Large, global information technology brands
37

Gartner Analysts Found (2014)
State of the Provider Market
38
A fragmented market and terminology :
 Software Tools
 Natural Language Processing
 Text Analysis Workbenches
 Social Analytics Dashboards
 Integrated Data Analysis Environments
 Solution Embedded Technologies

Your To-dos
for the next
session:
21.09.2015

To-do-List
40
Read the CID-Website with focus on Topic Analyst
as preparation for the upcoming guest lecture

Bonus points:
– Post questions
about the CID
software on
StudyNet
– No later than
Thursday noon

4
CID Topic Analyst
 Klick image – on webpage, bottom left, listen to audio (1 min)

5
CID Topic Analyst
Companion for Mobile
 Klick image – on webpage, bottom left, listen to audio (1 min)

6
 http://cid.com/images/mediacenter/broschures/topic-analyst.pdf
 http://cid.com/images/mediacenter/broschures/topic-analyst-
companion-for-mobile.pdf
CID Topic Analyst and Companion for Mobile
Recommended brochures

8
CID Topic Analyst
Benefits Live!

9
CID Topic Analyst
QuickDemo Aerospace

10
Webinar on CID Topic Analyst
Login data
 Adobe Connect for Video
 https://meet72195230.adobeconnect.com/topicanalystunistgallen/
 Conference Call for Audio
 Number: 0800 89 00 93
 Participation code: 73 42 03 04
The Webinar will be recorded and published on StudyNet.

Your to-dos
for the next
session:
28-Sep-2015

3 (of 5)
Bonus points:
– Post questions
about the webinar
on StudyNet
– No later than
Thursday noon

3
Overview
Use Cases
&
Technology

Guest Lecture
S. Paulutt
CID Consulting
(see separate slideset
on StudyNet)

5
Student Bonus Q&A – 1 (out of 10)
Mivelaz Vincent-Frédéric
If you had to name one weakness regarding
the Topic Analyst tool, what would it be?

6
Competitive Intelligence (CI)
Project Seminar: Enterprise 2.0 and Mobile Business
Universität St.Gallen
Comparison of the Topic-Analyst-based
Analysis with the Current Approach
- Using Digital Banking CI as an example
Projektteam: 4 HSG Students, Master of Business Innovation
Betreuung HSG: Christian Ruf, Lehrstuhl Prof. Dr. Andrea Back, Institut für Wirtschaftsinformatik (IWI)
Betreuung CID: Stephanie Paulutt
St. Gallen, March 19th, 2014
6

7
Current Practice ProcessesTopic-Analyst-based Process
 Time required
 Quality of Output
 Quality of Work-/Information Flow

8
Topic
Analyst
Analyst
News
provided by
crawler
Quick
dashboard
monitoring
(Use Case 1)
Selection of
topics for
the Deep
Dive
Doing the
Deep Dive
(Use Case 2)
TA supports
Deep Dive
Supervised
Topics
support
monitoring
Comments
and tagging
of articles in
TA
Presenting
the results
Topic-Analyst-based process
Use Case 1: Monitoring
Use Case 2: Deep Dive
TA: Topic Analyst

9
Monitoring – A challenge through the TA-process
Tags are very useful for collaborative work, but they need to be
predefined and used consistently!

10
Deep Dive – A suggested new feature
Documents, once in the Corpus, lose links
Improvement: Ability to import external documents
Bisher
Mit Topic
Analyst
Link
Topic Analyst
Original (if found via Google e.g.)

11
Student Bonus Q&A - 2
Bernauer Patrick
How can a customer analyse the effectiveness
of text analytics? What are typical
measurements/key figures?

12
Can text analysis effectively assess the quality of
«ideas» submitted to online idea contests?*
„Typical“ instance of an ideation
contest:
•430 users submitted
•725 ideas
• or 42‘094 words
• or 113 pages of plain text
(in Arial, 10)
Source: MySQL dump from a crowdsourcing platform. * PhD thesis Thomas Walter 2013

13
How to define ideation quality?
Idea quality usually consists of four distinct dimensions, but most important
in crowdsourcing (according to literature) is novelty:
Novelty Feasibility
Elabo-
ration
Strategic
relevance
Ideas should be…
• unique and rare,
• original and not yet expressed by anybody,
• not related with others,
• revolutionary and radical
• with ability to surprise
• imaginary and unexpected.

14
Text Mining Methodology
Text mining is the semi-automated process of extracting
patterns (useful information and knowledge) from large
amounts of unstructured data sources.
Text mining works by transposing words and phrases in unstructured data, such as
submissions to crowdsourcing websites, into numerical values (Pre-Processing)
which can then be analyzed with data mining techniques.
Pre-Processing
• Tokenization
• Stemming
• Part-of-speech tagging
• Stop word clearance
• Term Document Matrix
Text Mining (novelity)
• Frequency analysis
• Categorization
• Recognition of speech
• Clustering
• Sentiment Analysis
Visualization
• Tag Clouds
• Tree Maps
• Assoziation-graps
• Theme River
• Coloring

“I like computing. But I hate computing on
old computers.”
 Tokenization: breaking a stream of text up into words, phrases (here) or
symbols:
 1: I like computing.
 2: but I hate computing on old computers.
 Stemming: reducing inflected words to their stem:
 1: i like computing.
 2: but I hate computing on old computers.
 Stop-word cleaning: predefined list of so called stop-words are deletes:
 1: i like comput
 2: but I hate comput on old comput
 Part-of-speech tagging: marking up a word corresponding to the syntax
based on both its definition, as well as its context.
 1: like (adjective) comput (verb)
 2: hate (adjective) comput (verb) old (adjective) comput (verb) 15
The corpus is deconstructed during pre-pocessing

1. “I like computing. But I hate computing on
old computers.”
2. Term Document Matrix (TDM): describes the frequency of terms
which occur in a collection of text (the corpus). In a TDM, rows
correspond to documents (D) in the collection and columns
correspond to terms (T).
3. The TDM is the basis for applying text mining algorithms, e.g:
– Word-Frequency lists
– Text-Categorization
– Clustering/ pattern search
– Language recognition
– Sentiment analysis
16
TDM like comput hate old
pos verb verb verb adj
d1 1 1 0 0
d2 0 2 1 1
TDM is the basis of text mining

17
Automatic selection / recommendation
Idea 1: Select only single
case cluster

18
Automatic selection / recommendation
• Example: Contest of a clothing manufacturer
looking for self-cooling cloth for the olympics.
• Single item cluster defined by an idea
suggesting „super absorbant polymeres
(called SAPs)“.

19
Sukula Heini Elina
On average, which source of information (e.g.
web sites, social media, enterprise software) is
the most used one for text analysis?

22
A Model to help define relevant social media
Practice Project 2011, using IFA as an example
… – … - description – CI relevance

23
Balzer Lukas
How is the legal situation with web scraping?
How do you deal with it?

26
It is «complicated» – and (Swiss) law-in-progress
Details and current legal cases would be worth a BA- or MA-thesis
• No unique copyright law for data bases
• Protection under law for «Sammelwerke» (collective works) difficult
• To prosecute parasitic exploitation of others’ databases is very difficult,
and respective lawsuites are very risky
• Courts keep the freedom to «copy/imitate» very high
• That the law against unfair competition/practices (UWG) is effective,
needs verification
• Contractual solutions are encouraged

27
Cantù Rossana Violante
Where do you get the data from? Does Corpus
need to buy any (additional) data packages?
ABack @SPau: «Could you provide e.g 5 data sources that you
use for FREE (e.g. Wikipedia), and 5 data sources that you have
to pay for, and how much? (e.g. French dataset that is Wikipedia
like)“. Spau: – Sure, I will prepare a list.

28
Högdahl Louise Maria Irene
How does CID’s customer relationships look
like? Is Topic Analysis something your clients
use during a short time period or do they get
more interested as they start to use it?

29
IFA 2011 pilot - turned into a regular customer
Thrust of the 2011 pilot for IFA
Competitive Analysis
• Number of articles in media (traditional and Social
Media)
• Associations with Vendors und current Topics
Trendanalysis
• Current Trends in Consumer Electronics
Adhoc Analysis
• Media response to a Press Conference
2016
update
May 2011

30
Fontugne Louise
What are the issues with the law for companies
to analyse data? Are there data that companies
are not allowed to have/ analyse? What could
be the sanctions if companies have data that
they are not supposed to own?

31
Monitoring Employees’ eMail
The legal situation in Germany and Switzerland**
- details and current practices would be worth a BA- or MA-thesis
** quick assessment of a HSG law student, and what we could find –
HSG expert: Prof. Dr. Roland Müller, Titularprofessor für Arbeitsrecht
In general, systematic monitoring of the entire email traffic is not allowed.
It is considered undue. For supervising abuse e.g., milder methods like
sampling are a similarly effective approach.
Switzerland
• Absolutely forbidden is monitoring private email of employees. Even if the employer
forbids private mails, he is not allowed to “read” mails that are labeled as private. To
supervise the prohibition, it is only allowed to read the topic-line of emails.
• Monitoring/Reading business email is allowed, if it is reasonable, and employees must be
informed beforehand.
Germany
• If private email is not allowed, the employer may monitor all email, unless it is marked as
private.
• If private email is allowed, the employer is seen like a telecom provider and may not
monitor email (Telekommunikationsgesetz, Fernmeldegeheimnis)

32
Rivera Caballero Jaime Alejandro
You also have a product called Topic Analyst
for Mobile. This app is only available for IOS, is
that because it fits better with your target
market or are you considering also to include
android systems in the future?
ABack @SPau: «What criteria are relevant for the decision of which
App store to use?» Spau: Providing an app is still a competitive
advantage, as it contributes to differentiate our solution from
Other^vendors‘. Regarding Android, we follow customer
demand. As soon as more users demand it, we will provide it.“

33
Jüllig Hanna Kristina Elisabeth
According to your website, with Topic Analyst
companies can collect information from a huge
amount of channels, but at the same time only
get "the relevant" information. Who/what is it
that really decides what information is relevant?
And how to ensure your clients' technological
maturity?
«See the above mentioned model to define relevant
social media channels. The choices are usually made
in a consultative process with the customer.»
Another interesting example , how to define «relevant
Information» is our project to use text mining to help
filter relevant feedback given via the Lufthansa App.

34
Customer Feedback via Lufthansa Mobile App
Can textmining help to detect relevant customer feedback
automatically? *

35
Precision Recall* F1*
Categorizaation 38 % 61 % 46 %
Speech Recognition 54 % 92 % 68 %
Sentiment Recognition 51% 83 % 63 %
Project Result – Text Mining Dashboard

36
Cyriax Jörg Stephan
Do you work together with computer scientists
from universities to improve your product?
SPau: «Yes, we have a research partner, Prof. Dr. Gerhard Heyer
of University of Leipzig.
http://asv.informatik.uni-leipzig.de/staff/Gerhard_Heyer
The do not develop software components.
In my guest lecture, I will give insights into a current project.

7,814 Enterprise Applications of Text Intelligence
AutumnTerm ‘15 – Prof. Dr.Andrea Back, IWI-HSG

Use Cases
Clustering by application area*
Customer service and improved productivity most widely distributed
17
28
31
17
6
0
5
10
15
20
25
30
35
Competitive Intelligence Customer Service Improved User
Productivity
Operation Excellence Risk and Fraud
Management
# Distribution of application areas
* along:Yuen, D., Linden,A., & Koehler-Kruener,H. (2014).Technology Overview for Text Analytics. Gartner Inc.

Competitive/
Market
Intelligence

5
„Wiesenhof“ Lawyers find „Wiesengüggel“
and start a brand protection battle
http://www.bauernzeitung.ch/sda-archiv/2015/streit-um-marke-wiesengueggel-beigelegt/

6
mapegy
The Innovation Graph - mapping global innovation for everyone
Tobias Wagner - mapegy GmbH
wagner@mapegy.com
+49 (0)30 430 2212 0
www.mapegy.com

7
Innovation Graph
4
Retrieving, mapping and evaluating the global technology dynamics
● more than 5 Mio. institutions
(companies, universities, more than 100K startups)
● more than 50 Mio. experts
● Billion of networks, cooperations and clusters
● Billion of topics and technology fields
● more than 20 years of trends
● more than 200K locations
on basis of
● more than 100 Mio. patents from >150 patent offices
● more than 100 Mio. scientific publications
● more than 10K technical standards
● more than 1 Mio. press, product releases and social media publications daily
● and Mio. websites
Solution behind solution

8
Use Case Topic Industry Project type
Technology
Scouting
Various questions related to innovation management (see
use cases below)
Automobile
supplier
mapegy.scout
(users: 2)
Stakeholder
monitoring
Analysis of competitors’, suppliers’, customers’ know-how &
R&D activities in autonomous driving technologies
Automobile/
ICT industry
mapegy.radar
(users: 5)
Trend analysis
Identification of technology trends & new technology fields
in the field of clinical management systems
Health care
industry
mapegy.radar
(users: 3)
M&A,
Headhunting
Identification of potential acquisition candidates & domain
experts in display technologies
Consumer
electronics
mapegy.radar
(users: 3)
Portfolio
analysis
Evaluation of client’s fuel cell IP portfolio
(strengths/weaknesses) in order to buy missing knowhow
Automobile
mapegy.radar
(users: 2)
New business field
exploration
360° analysis of technologies, trends, stakeholders
in pipeline technologies
Oil & gas
industry
mapegy.radar
(users: 10)
Strategic Product
Management
Monitoring all environment-relevant features of competitor
‘s cars.
Automobile
mapegy.radar
(users: 10)
5
USE CASES
Customer success stories from diverse industries

9
USERS
6
Technology, Innovation & Portfolio-Managers
who want to develop their technology stack
Investors, M&A & Business analysts who want identify &
evaluate new technology business opportunities
Researchers & developers who want to explore
new technology frontiers
IP Professionals who want to make the best out of their IP
Government organizations who support regional development
HR Managers who are in search for the best technology experts
Everyone pushing technology
Sales & Market researchers or Product developers who want to understand
better technologies relevant for their markets or products

10
BENEFITS
Weeks of laborious research are condensed into minutes or even seconds
Having a finger on the pulse of time &
staying ahead of competition
Know market, competition,
dynamics
Map technologies with products
Identify right people
& partners
Faster to market
Identify threats &
opportunities
Save costs
7

13
10
Locate institutions and experts
globally/locally

14
Explore their cooperation
11

15
12
Analyze rankings for various KPIs

Customer
Service –Voice
of the Customer
«B2C, B2B»

Text Analytics at Swisscom
Initial situation
Reporting
Monthly manual
analysis (based on a
cutoff date) of the
service requests
using tally sheets
Recording
Agents record issues
as „service requests“
Signalling
Clients call Swisscom
and describe a
problem

§ Unreliable
§ Resource-intensive
§ Non-standardized
§ Incomplete
§ Time-delayed
Initial situation

Stakeholder needs
Initiation of a project to analyze service requests automatically and promptly (near
realtime)
Text Analytics provides the technical foundation for the project
Solution
1. Short periodicity & regular analyses
2. Detailed & granular problem clusters
3. Little effort
4. Automation
Business Requirements

SAS Text Analytics enables an end-to-end automation of the analysis process of
service requests
Daily & automatically
X = Y
Dataload
Daily
basis
Language
recognition
DE, FR, IT, EN?
Block
building
Units of
meaning
Synonym
recognition
Remote =
Zapper
Categori-
zation
Remote
belongs to TV
Evaluation
Trending topics/
comparison with
previous day
or period
21 3 4 5 6
What is possible through technology ...

0
2000
4000
6000
8000
10000
12000
#Messages
Senderreihenfolge
Bildstörung
Internetgeschwindigkeit
Internetausfall
Router
Issue Issue
New reportings enable fast detection of issues and trends
Order of stations
Image interference
Internet speed
Internet failure
Router
What changes ...

Customer
Service
«Social Media
Monitoring and
Sentiment»

23
Some 101 of Social Media Monitoring
Brand Monitoring. Easy
Slide:Courtesy of Dr.ThomasWalter,HSG alumnus (currentlyNamics AG)

24
…
Brand Monitoring. Medicore

25
Brand Monitoring. Hard

§ Posts are distributed via various channels (there is no
standard way of complaining, commenting and so on).
§ Post are heavily connected (“retweets”,“likes” etc. cause
redundancy).
§ Often responses take place on other channels.
§ Social media platforms undergo continuous change.
§ Significance of posts is context specific.
§ Language can be highly complex to measure (irony, sarcasm,
dialect, wordplay, language barriers, etc.).
§ But most important: Do you know what you are looking for?
Challenges of Monitoring Social Media

Open terms,low boundaries
Explicit terms,high classification
Posts found
Post
US postal service,
correspondence
Description
used in relevant
mentions (?!)
“The service of the post sucks so much, …”
“Read nice blog post from…”
“Guess what was in my post box today?”
Social Media MonitoringTradeoff

Classification and sentiment analysis are key text
mining techniques to leverage social media monitoring.
Sentiment140 –Twitter Sentiment Analysis

§ Again, it’s not that much about the technology you use, but about how
you address the topic.
§ After detailed analysis how social media talks about your brand, write
down a plan for you and your staff, including how to respond to:
§ Direct Questions
§ Positive Comments
§ Negative Comments
§ Shitstorms
§ Incorrect Information
§ Feedback
Respond Reply Reach outRetweet
Final thoughts

TopicAnalyst® Companion forWord and Outlook

3M 360 Encompass System
The 3M 360 Encompass System integrates
§ computer-assisted coding (CAC),
§ clinical documentation improvement (CDI),
§ concurrent quality metrics and analytics
into one application to
§ capture,
§ analyze
§ and advance
patient information across the care continuum

1. Analysis only once a month
2. Time-consuming and manual
process
3. Analyzed for a single
reference date
4. Error-proneness and
subjectivity
5. Just rough analyses possible
Until now UsingText Analytics
§ Daily evaluation
§ Fast automated process
§ Representative analysis
§ Standardized reports with
clear criteria
§ Early detection of problems &
trends
§ Detailed analysis of problem
clusters
Advantages

The introduction of the solution to reduce the number of tasks done by hand was
just the beginning.More ideas are ready to be brought to life.
§ Analysis by handYesterday
Today
And tomorrow?
Further developments
§ Automated analysis
§ Speech to text
§ Real-time analysis
§ Competitive intelligence
§ CRM – Enrichment of
customer profiles
Outlook

Risk and Fraud
Management
(our guest
lecture today)
@MBU: passendes Bild finden

Text mining steps
Recapitulation of online idea contest example
39
Pre-processing
Tokenization
Stemming
Part-of-speech tagging
Stop word clearance
Term Document Matrix
Text Mining (novelty)
Frequency analysis
Categorization
Recognition of speech
Clustering
Sentiment Analysis
Visualization
Tag Clouds
Tree Maps
Assoziation-graps
Theme River
Coloring

40
First step:text Acquisition,e.g.social data
Gnip -The World's Largest and MostTrusted Provider of Social Data

41
Excursus:Role of text data in «Big Data» ?

42
Further analytics step: Insights

Linguistic
techniques in
disambiguation
of texts

46
Linguistic techniques in disambiguation of texts
Qualified vocabularies, lexicons and synonyms
§ Are there recognized dictionaries of accepted terms?
§ Are there alternate spellings for a given word?
§ Is there a thesaurus of synonyms?
Drill ?

47
Grammar and syntax
§ Each language has its own system, structure and rules of grammar.
§ The syntactic relationship of words to their surroundings must be
deciphered and interpreted through the relationship of words to their
surrounding.
§ Even simple word order changes may significantly change the meaning
of a sentence.

48
Standardization
What provision is there to resolve known variations for a term to a
single, agreed “core” value or to recognized hierarchies?
=

49
Character and tone
§ What is the underlying intended pitch or inflection of the statement?
§ What intonation and attitude are being conveyed?

50
Literal and figurative language
§ Is the phrase precise and direct in its meaning,or is there some form
of indirect connotation?

Tools for Social
Media Monitoring
&Text Mining

Tools
Exemplary enterprise solution:Adobe Social

Tools
There are many many more.Try them!
Text Mining within a Digital Marketing System:
§ E.g.Adobe Social http://www.adobe.com/ch_de/products/social.html
Special Software/ Projects:
§ GATE http://gate.ac.uk/
§ Rapid Miner http://rapid-i.com/content/view/181/190/
§ QDA Miner http://provalisresearch.com/
§ Megaputer http://www.megaputer.com/site/index.php
§ Liste anTools http://www.kdnuggets.com/software/text.html
Web-tools with Text-/ Data- Mining Analysis (short list):
§ Weblyzard http://www.weblyzard.com/
§ Brandwatch http://www.brandwatch.com/
§ Sysomos http://www.sysomos.com/
§ Sentiment140 http://www.sentiment140.com/
§ Sentiment viz http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/
§ Wordle http://www.wordle.net/
55

Guest Lecture
K. Schwenke
KPMG,Technical
Forensics
(see separate slideset on
StudyNet)

Threefold task for each workgroup
2
1 - PREPARATION
- questions/tasks see following slides
- Submit 1-2 p. to andrea.back@unisg.ch
2 - ON-SITE AT EXCURSION
- topic ad-hoc defined by host @ excursion
- «submission» is your oral participation
3 - FOLLOW UP CREDIT-TASK
- topic and format defined @ excursion
- submit/email (ppt, doc, …) to ABack only

Recap: Planning your time (3 credits – 90 hours)
Our estimate
Task
Estimated hrs
per person
Actual hrs
6/7/8/9 – Enterprise Application Visit (I. of 4) 13 **
6/7/8/9 – Enterprise Application Visit (II. of 4) 13 **
10 – Guest lecture Audi AG, From TA-Idea to
Adoption, Guest lecture SwissRe, Course
wrap up and final steps
6
Groupwork completion and finalizing excursion
reports
24
3 days in other
words
3
** about 4-6 hrs for the three
preparatory questions

1 – Preparation: What to submit
To document your preparatory work, please submit a
max. 1-2 page text document
describing:
• HOW you found your results
• WHICH of your findings you found
most interesting
• GLOSSARY TERMS that you suggest
to add, including your short definition
4

2 – On-site at excursion – bring your notebook
Every host was asked to include a 45-60 min. interactive part,
in which (especially) the groups make a contribution.
Expect designs like these:
• Discussion groups with presenting
• using a flipchart
• using moderation cards
• using a mind-mapping tool on your notebook
• …
• Interactive Q&A session – blocked or intermittent
• …
but be open, since this is up to the host and beyond my control
5

3 – Follow-up CREDIT-TASK
To document your follow-up work, please submit a
documentation in a format that suits the defined topic and what
was agreed upon at the excursion
• TIME-TO-INVEST: As a guideline, use the budgeted time per person
that I gave in Unit 1
• WHAT THE HOST GETS: You will submit your work to ABack only.
She will decide whether the work will be forwarded to the host company.
If she forwards it to the host, all authors will be included in mail-CC, so
you get notified about it.
• SUITABLE FORMAT: The format should suit the topic
as well as the style/wishes/culture of the host. Some might like PPT
slides with annotations, others might like a text document.
Even Prezis or videos can be an option. Make your choice.
6

Both Google groups, each please prepare
7
1. What companies does the newly formed Alphabet Holding
entail? What are their missions?
2. What improvement did the acquisition of Metaweb bring to
Google? And how is it related to text analytics/analysis? Here a
good video for that
3. What terms (and short explanations) do you suggest to add to
our Glossary?
14:15 – 18:00 Google Office, Zürich, Brandschenke-Str. 110
Patrick Warnking, Yves Brunschwiler, Philipp Probst

I - Google Search: Define xxtermxx» Assessment
8
1. Goal: The Google search function «Define xxtermxx will be tested
and assessed. The characteristics of the results will be described,
and the quality of the results will be evaluated along criteria
chosen by the group.
2. All glossary terms (initial ones and suggestions in the preparatory
work (till end-of Nov.)) will be «google-define-searched».
3. A glossary to use for the next course will be curated; the student
group defines the format (need not be a *.doc or *.pdf)
4. Members: HSG and Guest Students

II – Brand Competitive Analysis with Google Tools
A Comparison of CEMS Partner Universities
9
1. Brand competitive analysis of CEMS
universities: University of St Gallen
compared with the 6 following universities:
Copenhaguen Business School (CBS), ESADE,
HEC Paris, London School of Economics
(LSE), Rotterdam School of Management (RSM)
and Bocconi.
2. Analysis using the tools presented at the
Google excursion, Nov. 9th, by Philipp
Probst <pprobst@google.com>
3. Members: HSG and Guest Students

Both Swisscom groups, each please prepare
10
1. Find out what API economy means. Does it have relations to text
analysis technology/- methodology/- business?
2. What questions would you ask Swisscom to find out whether
they provide API services and what their plans are to identify and
use API economy opportunities (for their own data)?
3. What terms (and short explanations) do you suggest to add to
our Glossary?
14:15 – 18:00 Swisscom Office, 8005 Zürich, Pfingstweidstrasse 51, room 0.05
Dr. Falk Kohlmann, Lukas Peter, Kay Lummitsch, (N.N. Squirro)

11
Swisscom Team I
Everybody speaks about "Digital Transformation". Find out how the
DT relates to APIs and which API products Swisscom shall offer to
its Swiss customers & partners to make them & Swisscom
successful in the digital world. (Questions, if needed, to Kay
Lummitsch)

12
Swisscom Team II
Financial Industry. Digitalization is transforming the financial
industry. With the e-foresight Think Tank, we identify at Swisscom
new trends, analyze the impact on Swiss Retail Banking and
accompany our customer banks on this path with high-class
research. Find out how we can use a Big Data / Text Analysis Tool,
such as Squirro, to provide new products/offerings regarding trend
research for our customer banks. (Questions, if needed, to Dr. Falk
Kohlmann)

Both AXA groups, each please prepare
13
1. AXA uses teams in India for its CI research purposes. Find out several
companies in India (or other overseas countries) that offer such
services. How do these services differ?
2. AXA is present in several social media platforms. Find out in which and
what AXA does there (last 6 months)
3. What terms (and short explanations) do you suggest to add to our
Glossary?
14:00 – 18:00 AXA Office, Winterthur, Superblock, Pionierstrasse 3
Dr. Florian Hamel, Gaetano Mecenero, Lukas Wille, Andreas Wendt

14
Both teams same task, but different firms
Your preparatory task was: “AXA is present in several social media platforms.
Find out in which and what AXA does there (last 6 months)”.
Now expand your analyses by including other insurance companies’ social
media activities on: Linkedin, Xing – Facebook, Google+ - Youtube -
Instagram - Pinterest. Your comparative study with AXA should include
quantitative as well as qualitative assessments.
• Group 1: Insurance firms USA: Progressiv – Geico – Prudential
• Group 2: Insurance firms CH: Mobiliar – Zürich – Allianz
The two groups please agree upon and use common analysis criteria to allow
to combine the results. But write your discussion/conclusions independently.
PS: You may explore the online textmining tool http://www.minemytext.com
and decide, whether you get and add insights to your analysis gained by this
tool (voluntary).

SIKA group, please prepare
15
1. Find out what “Predictive Analytics” means. Does it have relations to
text analysis technology, -methods, -business? If you think yes, please
elaborate.
2. What questions would you ask Sika to find out whether they use
Predictive Analytics, and what their plans are to identify and use
Predictive Analytics opportunities?
3. What terms (and short explanations) do you suggest to add to our
Glossary?
14:15 – 18:00 Sika Office, Zürich, Tüffenwies 16
Christian Frey, Jacqueline Vo

16
SIKA-Task: Visualizations in Market Monitoring
What would be the added value of
a) visualizations and
b) continuous monitoring mode
for typical Sika market intelligence reports. As an example, use the
Euroconstruct Summary Report.
• Consider what is visually possible especially with software
solutions like CID, Squirro and Mapegy.
• Present your ideas in kind of a “mock-up” dashboard, to also
make your presentation in visual style.

Agenda
14:15 – 14:30 – Course Wrap Up
• Welcome Bruno Zanvit
• Credits and Grading: Written Exam – Bonus Points/Prep Work – Credit Group Task
• Evaluation Forms emailed to You
14:30 – 15:30 – Guest Lecture Dr. S. Scholer, Strategische
Unternehmensplanung, Audi
Text Mining to Support Strategy Work
15:30 – 16:00; – 16:15 – Break Activities; Project venue CID
• Break, including filling in Evaluation Forms
• Implementation of a Project with Topic Analyst
16:15 – 17:15 – Guest Lecture Chr. Smiela, Life & Health Information
Architecture & Integration, Swiss Reinsurance Company
Text Analytics in Underwriting – Use Cases and Next Gen Platform
17:15 – 17:45 – Last not Least & Good Bye 2

Q&As about
- Bruno Zanvit
- Credits & Grading
Questions
- Course Evaluation
4

Guest Lecture
Dr. S. Scholer
Audi
on StudyNet)

Guest Lecture
Chr. Smiela
Swiss Re
on StudyNet)

7
Implementation of a project
with Topic Analyst®
Overview of the main steps
December 2015

8
Definition of use cases and scope (1/2)
CID provides a checklist to the
customer to define and prioritize
use cases and the scope.

9
Definition of use cases and scope (2/2)

10
Organizational aspects and communication
* Topic Analyst power users
** Recipients of push-alerts, dashboards,
newsletters etc. provided by the analyst team

11
Project implementation plan
1. Order and scope validation (customer)
2. Configuration of sources (crawlers, importers) (CID)
3. Adaptation of the Knowledge Base (CID)
3. Initial Crawling (CID)
4. Tuning (CID)
5. First milestone presentation to power users (both)
• Match with the major use cases (both)
6. Finetuning (both)
7. Initial dashboard preparation for end-users & training (CID/ customer)
8. Jour fixe to discuss questions, tool features, new use cases etc. (both)

12
Stephanie Paulutt
CID GmbH
Struthweg 1
D-63594 Hasselroth
Germany
www.cid.com
Tel.: +49 6051-8846-111
E.: s.paulutt@cid.de
twitter: @CIDGermany
http://cid.com/products/topic-analyst-ecosystem

What else?
- CID: Project steps
with customers
- MA-theses
- Getting hired:
Christian Lohri,
Swiss Re:
Video-Self-
Interview
(German)
14

Merry
Christmas
&
Happy New
Year
15

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