Text Analytics Applied (LIDER roadmapping presentation)

Text Analytics Applied
Seth Grimes
Alta Plana Corporation
@sethgrimes
2nd LIDER roadmapping
workshop – Madrid
May 8, 2014

2nd LIDER workshop
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“Organizations embracing text analytics all
report having an epiphany moment when
they suddenly knew more than before.”
-- Philip Russom, the Data Warehousing Institute, 2007
http://tdwi.org/articles/2007/05/09-what-works/bi-search-and-text-analytics.aspx

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Document
input and
processing
Knowledge
handling is
key
Desk Set (1957): Computer engineer
Richard Sumner (Spencer Tracy)
and television network librarian
Bunny Watson (Katherine Hepburn)
and the "electronic brain" EMERAC.
Hans Peter Luhn
“A Business Intelligence System”
IBM Journal, October 1958

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Statistics and semantics
Text analytics involves statistical characterization and
semantic understanding of text-derived features –
Named entities: people, companies, places, etc.
Pattern-based entities: e-mail addresses, phone numbers, etc.
Concepts: abstractions of entities.
Facts and relationships.
Events.
Concrete and abstract attributes (e.g., “expensive” &
“comfortable”) including measure-value pairs.
Subjectivity in the forms of opinions, sentiments, and
emotions: attitudinal data.
– applied to business ends.

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Sources
It’s a truism that 80% of enterprise-relevant information
originates in “unstructured” form:
E-mail and messages.
Web pages, online news & blogs, forum postings, and other
social media.
Contact-center notes and transcripts.
Surveys, feedback forms, warranty claims.
Scientific literature, books, legal documents.
...
Non-text “unstructured” content?
Images
Audio including speech
Video
Value derives from patterns.

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Value
What do we do with information online, on-social, and in the
enterprise?
1. Post/Publish, Manage, and Archive.
2. Index and Search.
3. Categorize and Classify according to metadata &
contents.
4. Extract and Analyze.

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Semantics, analytics, and IR
Text analytics generates semantics to bridge search, BI, and
applications, enabling next-generation information
systems.
Search
BI/Big
Data
Applica-
tions
Search based
applications
(search + text +
apps)
Information access
(search + analytics)
Synthesis (text +
BI)/(big data)
Text analytics
(inner circle)
Semantic search
(search + text)
NextGen CRM, EFM,
MR, marketing,
apps…

New York Times,
September 8, 1957

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http://open.blogs.nytimes.com/2012/02/16/rnews-is-
here-and-this-is-what-it-means/
<div itemscope itemtype="http://schema.org/Organization">
<span itemprop="name">Google.org (GOOG)</span>
Contact Details:
<div itemprop="address" itemscope itemtype="http://schema.org/PostalAddress">
Main address:
<span itemprop="streetAddress">38 avenue de l'Opera</span>
<span itemprop="postalCode">F-75002</span>
<span itemprop="addressLocality">Paris, France</span> ,
</div>
Tel:<span itemprop="telephone">( 33 1) 42 68 53 00 </span>,
Fax:<span itemprop="faxNumber">( 33 1) 42 68 53 01 </span>,
E-mail: <span itemprop="email">secretariat(at)google.org</span>
</div>
http://schema.org/Organization
Structure matters
http://img.freebase.com/api/trans/raw/m/02dtnzv
http://www.cambridgesemantics.com/se
mantic-university/semantic-search-and-
the-semantic-web

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Exploratory analysis, synthesis
Decisive Analytics
http://www.dac.us/

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http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html
A big data analytics architecture (example)

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Applications
Synthesis is cool, but let’s take a step back…
Text analytics has applications in:
Intelligence & law enforcement.
Life sciences & clinical medicine.
Media & publishing including social-media analysis and
contextual advertizing.
Competitive intelligence.
Voice of the Customer: CRM, product management &
marketing.
Public administration & policy.
Legal, tax & regulatory (LTR) including compliance.
Recruiting.

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Sentiment analysis
A specialization, of relevance to:
Brand/reputation management.
Customer experience management (CEM).
Competitive intelligence.
Survey analysis (EFM).
Market research.
Product design/quality.
Trend spotting.

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http://altaplana.com/TA2014

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5%
6%
8%
9%
10%
11%
13%
14%
15%
16%
25%
27%
29%
33%
38%
38%
39%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Military/national security/intelligence
Law enforcement
Intellectual property/patent analysis
Financial services/capital markets
Product/service design, quality assurance, or warranty claims
Other
Insurance, risk management, or fraud
E-discovery
Life sciences or clinical medicine
Online commerce including shopping, price intelligence, reviews
Content management or publishing
Customer /CRM
Search, information access, or Question Answering
Competitive intelligence
Brand/product/reputation management
Research (not listed)
Voice of the Customer / Customer Experience Management
What are your primary applications where text comes into play?

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Voice of the Customer
Text analytics is applied to improve customer service and
boost satisfaction and loyalty.
Analyze customer interactions and opinions –
• E-mail, contact-center notes, survey responses.
• Forum & blog posting and other social media.
– to –
• Address customer product & service issues.
• Improve quality.
• Manage brand & reputation.
Assessment of qualitative information from text helps users –
• Gain feedback on interactions.
• Assess customer value.
• Understand root causes.
• Mine data for measures such as churn likelihood.

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Online commerce
Text analytics is applied for marketing, search optimization,
competitive intelligence.
Analyze social media and enterprise feedback to understand
the Voice of the Market:
• Opportunities
• Threats
• Trends
Categorize product and service offerings for on-site search
and faceted navigation and to enrich content delivery.
Annotate pages to enhance Web-search findability, ranking.
Scrape competitor sites for offers and pricing.
Analyze social and news media for competitive information.

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E-Discovery and compliance
Text analytics is applied for compliance, fraud and risk, and
e-discovery.
Regulatory mandates and corporate practices dictate –
• Monitoring corporate communications
• Managing electronic stored information for production in
event of litigation
Sources include e-mail (!!), news, social media
Risk avoidance and fraud detection are key to effective
decision making
• Text analytics mines critical data from unstructured sources
• Integrated text-transactional analytics provides rich insights

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5%
5%
5%
5%
7%
9%
11%
11%
12%
12%
12%
13%
16%
19%
20%
20%
22%
26%
31%
31%
32%
36%
37%
38%
42%
43%
46%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
insurance claims or underwriting notes
point-of-service notes or transcripts
video or animated images
warranty claims/documentation
photographs or other graphical images
crime, legal, or judicial reports or evidentiary materials
field/intelligence reports
speech or other audio
patent/IP filings
other
text messages/instant messages/SMS
medical records
Web-site feedback
social media not listed above
chat
employee surveys
contact-center notes or transcripts
e-mail and correspondence
online reviews
scientific or technical literature
Facebook postings
on-line forums
customer/market surveys
comments on blogs and articles
news articles
blogs (long form) including Tumblr
Twitter, Sina Weibo, or other microblogs
What textual information are you analyzing or do you plan to
analyze?

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16%
19%
20%
20%
22%
26%
31%
31%
32%
36%
37%
38%
42%
43%
46%
0% 10% 20% 30% 40% 50% 60% 70%
Web-site feedback
social media not listed above
chat
employee surveys
contact-center notes or transcripts
e-mail and correspondence
online reviews
scientific or technical literature
Facebook postings
on-line forums
customer/market surveys
comments on blogs and articles
news articles
blogs (long form) including Tumblr
Twitter, Sina Weibo, or other microblogs
What textual information are you analyzing or do you plan to
analyze?
2014
2011
2009

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Current, 33%
Current, 31%
Current, 34%
Current, 47%
Current, 51%
Current, 56%
Current, 47%
Current, 54%
Current, 66%
Expect, 21%
Expect, 24%
Expect, 23%
Expect, 23%
Expect, 28%
Expect, 25%
Expect, 33%
Expect, 28%
Expect, 22%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Events
Semantic annotations
Other entities – phone numbers, part/product numbers, e-mail &
street addresses, etc.
Metadata such as document author, publication
date, title, headers, etc.
Concepts, that is, abstract groups of entities
Named entities – people, companies, geographic
locations, brands, ticker symbols, etc.
Relationships and/or facts
Sentiment, opinions, attitudes, emotions, perceptions, intent
Topics and themes
Do you currently need (or expect to need) to extract or analyze...

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16%
18%
22%
25%
28%
30%
32%
33%
33%
36%
37%
40%
41%
43%
44%
45%
53%
53%
54%
64%
0% 10% 20% 30% 40% 50% 60% 70%
export to Semantic Web formats…
frontline voice of the customer (VOC) system integration
media monitoring/analysis interface
hosted or Web service (on-demand "API") option
supports data fusion / unified analytics
sector adaptation (e.g., hospitality, insurance, retail, health…
BI (business intelligence) integration
ability to create custom workflows or to create or change…
big data capabilities, e.g., via Hadoop/MapReduce
predictive-analytics integration
open source
support for multiple languages
sentiment scoring
"real time" capabilities
low cost
deep sentiment/emotion/opinion/intent extraction
document classification
broad information extraction capability
ability to use specialized…
ability to generate categories or taxonomies
What is important in a solution?

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10%
1%
16%
9%
36%
34%
2%
2%
18%
7%
4%
3%
13%
8%
7%
38%
3%
2%
3%
2%
5%
9%
17%
3%
28%
7%
17%
24%
2%
10%
11%
15%
8%
4%
17%
21%
3%
20%
4%
0%
1%
1%
2%
0%
0% 10% 20% 30% 40% 50% 60%
Arabic
Bahasa Indonesia or Malay
Chinese
Dutch
French
German
Greek
Hindi, Urdu, Bengali, Punjabi, or other…
Italian
Japanese
Korean
Polish
Portuguese
Russian
Scandinavian or Baltic
Spanish
Turkish or Turkic
Other African
Other Arabic script (including…
Other East Asian
Other European or Slavic/Cyrillic
Other
Current
Within 2 years
Non-English language support?

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Software & platform options
Text-analytics options may be grouped in general classes.
• Installed text-analysis application, whether desktop or
server or deployed in-database.
• Data mining workbench.
• Hosted.
• Programming tool.
• As-a-service, via an application programming interface
(API).
• Code library or component of a business/vertical
application, for instance for CRM, e-discovery, search.
Text analytics is frequently embedded in search or other
end-user applications.
The slides that follow next will present leading options in
each category except Hosted…

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User decision criteria
Primary considerations include –
Adaptation or specialization: To a business or cultural domain,
language, information type (e.g., text, speech, images) &
source (e.g., Twitter, e-mail, online news).
By-user customization possibilities: For instance, via custom
taxonomies, rules, lexicons.
Sentiment resolution: Aggregate, message, or feature level.
(What features? Topics, coreferenced entities?)
What sentiment? Valence & what else? Emotion? Intent?
Outputs: E.g., annotated text, models, indicators, dashboards,
exploratory data interfaces.
Usage mode: As-a-service (API), installed, or hosted/cloud.
Capacity: Volume, performance, throughput, latency.
Cost.

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Linked Data Links?

Text Analytics Applied (LIDER roadmapping presentation)

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