SlideShare ist ein Scribd-Unternehmen logo
1 von 28
Text Analytics Applied
Seth Grimes
Alta Plana Corporation
@sethgrimes
2nd LIDER roadmapping
workshop – Madrid
May 8, 2014
Text Analytics Applied
2nd LIDER workshop
2
“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
Text Analytics Applied
2nd LIDER workshop
3
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
Text Analytics Applied
2nd LIDER workshop
5
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.
Text Analytics Applied
2nd LIDER workshop
6
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.
Text Analytics Applied
2nd LIDER workshop
7
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.
Text Analytics Applied
2nd LIDER workshop
8
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
Text Analytics Applied
2nd LIDER workshop
10
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
Text Analytics Applied
2nd LIDER workshop
11
Exploratory analysis, synthesis
Decisive Analytics
http://www.dac.us/
Text Analytics Applied
2nd LIDER workshop
12
http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html
A big data analytics architecture (example)
Text Analytics Applied
2nd LIDER workshop
13
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.
Text Analytics Applied
2nd LIDER workshop
14
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.
Text Analytics Applied
2nd LIDER workshop
15
http://altaplana.com/TA2014
Text Analytics Applied
2nd LIDER workshop
16
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?
Text Analytics Applied
2nd LIDER workshop
17
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.
Text Analytics Applied
2nd LIDER workshop
18
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.
Text Analytics Applied
2nd LIDER workshop
19
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
Text Analytics Applied
2nd LIDER workshop
20
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?
Text Analytics Applied
2nd LIDER workshop
21
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
Text Analytics Applied
2nd LIDER workshop
22
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...
Text Analytics Applied
2nd LIDER workshop
23
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?
Text Analytics Applied
2nd LIDER workshop
24
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?
Text Analytics Applied
2nd LIDER workshop
25
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…
Text Analytics Applied
2nd LIDER workshop
26
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.
Text Analytics Applied
2nd LIDER workshop
27
Linked Data Links?
Text Analytics Applied
Seth Grimes
Alta Plana Corporation
@sethgrimes
2nd LIDER roadmapping
workshop – Madrid
May 8, 2014

Weitere ähnliche Inhalte

Was ist angesagt?

Popular Text Analytics Algorithms
Popular Text Analytics AlgorithmsPopular Text Analytics Algorithms
Popular Text Analytics AlgorithmsPromptCloud
 
Product Sentiment Analysis
Product Sentiment AnalysisProduct Sentiment Analysis
Product Sentiment Analysisnancy amala
 
A Survey Of Collaborative Filtering Techniques
A Survey Of Collaborative Filtering TechniquesA Survey Of Collaborative Filtering Techniques
A Survey Of Collaborative Filtering Techniquestengyue5i5j
 
2016 Data Science Salary Survey
2016 Data Science Salary Survey2016 Data Science Salary Survey
2016 Data Science Salary SurveyTrieu Nguyen
 
When to use the different text analytics tools - Meaning Cloud
When to use the different text analytics tools - Meaning CloudWhen to use the different text analytics tools - Meaning Cloud
When to use the different text analytics tools - Meaning CloudMeaningCloud
 
O’reilly media 2014 data-science-salary-survey
O’reilly media 2014 data-science-salary-surveyO’reilly media 2014 data-science-salary-survey
O’reilly media 2014 data-science-salary-surveyAdam Rabinovitch
 
Introduction to data science and candidate data science projects
Introduction to data science and candidate data science projectsIntroduction to data science and candidate data science projects
Introduction to data science and candidate data science projectsJay (Jianqiang) Wang
 
Course - Machine Learning Basics with R
Course - Machine Learning Basics with R Course - Machine Learning Basics with R
Course - Machine Learning Basics with R Persontyle
 
Sentiment Analysis on Twitter Dataset using R Language
Sentiment Analysis on Twitter Dataset using R LanguageSentiment Analysis on Twitter Dataset using R Language
Sentiment Analysis on Twitter Dataset using R Languageijtsrd
 
Detecting Gender-bias from Energy Modeling Jobscape
Detecting Gender-bias from Energy Modeling JobscapeDetecting Gender-bias from Energy Modeling Jobscape
Detecting Gender-bias from Energy Modeling Jobscapeyungahhh
 
Relevancy and Search Quality Analysis - Search Technologies
Relevancy and Search Quality Analysis - Search TechnologiesRelevancy and Search Quality Analysis - Search Technologies
Relevancy and Search Quality Analysis - Search Technologiesenterprisesearchmeetup
 
Deep Recommender Systems - PAPIs.io LATAM 2018
Deep Recommender Systems - PAPIs.io LATAM 2018Deep Recommender Systems - PAPIs.io LATAM 2018
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
 
Theo downes le guin - listening - 2011
Theo downes le guin - listening - 2011Theo downes le guin - listening - 2011
Theo downes le guin - listening - 2011Ray Poynter
 
Data Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesData Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
 
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: TwisentIRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: TwisentIRJET Journal
 
Personalizing Search
Personalizing SearchPersonalizing Search
Personalizing SearchCognizant
 
Final Poster for Engineering Showcase
Final Poster for Engineering ShowcaseFinal Poster for Engineering Showcase
Final Poster for Engineering ShowcaseTucker Truesdale
 

Was ist angesagt? (20)

Popular Text Analytics Algorithms
Popular Text Analytics AlgorithmsPopular Text Analytics Algorithms
Popular Text Analytics Algorithms
 
Product Sentiment Analysis
Product Sentiment AnalysisProduct Sentiment Analysis
Product Sentiment Analysis
 
A Survey Of Collaborative Filtering Techniques
A Survey Of Collaborative Filtering TechniquesA Survey Of Collaborative Filtering Techniques
A Survey Of Collaborative Filtering Techniques
 
Social Data Mining
Social Data MiningSocial Data Mining
Social Data Mining
 
2016 Data Science Salary Survey
2016 Data Science Salary Survey2016 Data Science Salary Survey
2016 Data Science Salary Survey
 
When to use the different text analytics tools - Meaning Cloud
When to use the different text analytics tools - Meaning CloudWhen to use the different text analytics tools - Meaning Cloud
When to use the different text analytics tools - Meaning Cloud
 
O’reilly media 2014 data-science-salary-survey
O’reilly media 2014 data-science-salary-surveyO’reilly media 2014 data-science-salary-survey
O’reilly media 2014 data-science-salary-survey
 
Introduction to data science and candidate data science projects
Introduction to data science and candidate data science projectsIntroduction to data science and candidate data science projects
Introduction to data science and candidate data science projects
 
Course - Machine Learning Basics with R
Course - Machine Learning Basics with R Course - Machine Learning Basics with R
Course - Machine Learning Basics with R
 
Sentiment Analysis on Twitter Dataset using R Language
Sentiment Analysis on Twitter Dataset using R LanguageSentiment Analysis on Twitter Dataset using R Language
Sentiment Analysis on Twitter Dataset using R Language
 
Project report
Project reportProject report
Project report
 
Big Data @ CBS
Big Data @ CBSBig Data @ CBS
Big Data @ CBS
 
Detecting Gender-bias from Energy Modeling Jobscape
Detecting Gender-bias from Energy Modeling JobscapeDetecting Gender-bias from Energy Modeling Jobscape
Detecting Gender-bias from Energy Modeling Jobscape
 
Relevancy and Search Quality Analysis - Search Technologies
Relevancy and Search Quality Analysis - Search TechnologiesRelevancy and Search Quality Analysis - Search Technologies
Relevancy and Search Quality Analysis - Search Technologies
 
Deep Recommender Systems - PAPIs.io LATAM 2018
Deep Recommender Systems - PAPIs.io LATAM 2018Deep Recommender Systems - PAPIs.io LATAM 2018
Deep Recommender Systems - PAPIs.io LATAM 2018
 
Theo downes le guin - listening - 2011
Theo downes le guin - listening - 2011Theo downes le guin - listening - 2011
Theo downes le guin - listening - 2011
 
Data Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesData Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics Capabilities
 
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: TwisentIRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
 
Personalizing Search
Personalizing SearchPersonalizing Search
Personalizing Search
 
Final Poster for Engineering Showcase
Final Poster for Engineering ShowcaseFinal Poster for Engineering Showcase
Final Poster for Engineering Showcase
 

Ähnlich wie Text Analytics Applied (LIDER roadmapping presentation)

Knowledge Extraction from Social Media
Knowledge Extraction from Social MediaKnowledge Extraction from Social Media
Knowledge Extraction from Social MediaSeth Grimes
 
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
 
Diy research trends webinar(2) revised(2)
Diy research trends webinar(2) revised(2)Diy research trends webinar(2) revised(2)
Diy research trends webinar(2) revised(2)QuestionPro
 
Machine learning at b.e.s.t. summer university
Machine learning  at b.e.s.t. summer universityMachine learning  at b.e.s.t. summer university
Machine learning at b.e.s.t. summer universityLászló Kovács
 
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET Journal
 
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET Journal
 
Opportunities and methodological challenges of Big Data for official statist...
Opportunities and methodological challenges of  Big Data for official statist...Opportunities and methodological challenges of  Big Data for official statist...
Opportunities and methodological challenges of Big Data for official statist...Piet J.H. Daas
 
Data Science for Internet of Things with Ajit Jaokar
Data Science for Internet of Things with Ajit JaokarData Science for Internet of Things with Ajit Jaokar
Data Science for Internet of Things with Ajit JaokarJessica Willis
 
OSINT using Twitter & Python
OSINT using Twitter & PythonOSINT using Twitter & Python
OSINT using Twitter & Python37point2
 
Il ruolo chiave degli Advanced Analytics per la Supply Chain
Il ruolo chiave degli Advanced Analytics per la Supply ChainIl ruolo chiave degli Advanced Analytics per la Supply Chain
Il ruolo chiave degli Advanced Analytics per la Supply ChainACTOR
 
ACTOR - "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...
ACTOR -  "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...ACTOR -  "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...
ACTOR - "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...logisticaefficiente
 
Session I - Big Data F. Bianchi, F. Scalfati, Text mining and machine l...
Session I  -  Big Data    F. Bianchi, F. Scalfati, Text mining and machine  l...Session I  -  Big Data    F. Bianchi, F. Scalfati, Text mining and machine  l...
Session I - Big Data F. Bianchi, F. Scalfati, Text mining and machine l...Istituto nazionale di statistica
 
Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...
Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...
Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...IRJET Journal
 
An approach for evaluation of social...
An approach for evaluation of social...An approach for evaluation of social...
An approach for evaluation of social...STIinnsbruck
 

Ähnlich wie Text Analytics Applied (LIDER roadmapping presentation) (20)

Knowledge Extraction from Social Media
Knowledge Extraction from Social MediaKnowledge Extraction from Social Media
Knowledge Extraction from Social Media
 
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
 
Diy research trends webinar(2) revised(2)
Diy research trends webinar(2) revised(2)Diy research trends webinar(2) revised(2)
Diy research trends webinar(2) revised(2)
 
Machine learning at b.e.s.t. summer university
Machine learning  at b.e.s.t. summer universityMachine learning  at b.e.s.t. summer university
Machine learning at b.e.s.t. summer university
 
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
 
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
 
Certified Business Analytics Specialist (CBAS)
Certified Business Analytics Specialist (CBAS) Certified Business Analytics Specialist (CBAS)
Certified Business Analytics Specialist (CBAS)
 
Opportunities and methodological challenges of Big Data for official statist...
Opportunities and methodological challenges of  Big Data for official statist...Opportunities and methodological challenges of  Big Data for official statist...
Opportunities and methodological challenges of Big Data for official statist...
 
Ajit jaokar slides
Ajit jaokar slidesAjit jaokar slides
Ajit jaokar slides
 
Data Science for Internet of Things with Ajit Jaokar
Data Science for Internet of Things with Ajit JaokarData Science for Internet of Things with Ajit Jaokar
Data Science for Internet of Things with Ajit Jaokar
 
OSINT using Twitter & Python
OSINT using Twitter & PythonOSINT using Twitter & Python
OSINT using Twitter & Python
 
Il ruolo chiave degli Advanced Analytics per la Supply Chain
Il ruolo chiave degli Advanced Analytics per la Supply ChainIl ruolo chiave degli Advanced Analytics per la Supply Chain
Il ruolo chiave degli Advanced Analytics per la Supply Chain
 
ACTOR - "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...
ACTOR -  "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...ACTOR -  "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...
ACTOR - "Il ruolo chiave degli Advanced Analytics per la Supply Chain. Intel...
 
Opra W2&4 Tech Essentials
Opra W2&4 Tech EssentialsOpra W2&4 Tech Essentials
Opra W2&4 Tech Essentials
 
Session I - Big Data F. Bianchi, F. Scalfati, Text mining and machine l...
Session I  -  Big Data    F. Bianchi, F. Scalfati, Text mining and machine  l...Session I  -  Big Data    F. Bianchi, F. Scalfati, Text mining and machine  l...
Session I - Big Data F. Bianchi, F. Scalfati, Text mining and machine l...
 
Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...
Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...
Detecting Malicious Bots in Social Media Accounts Using Machine Learning Tech...
 
An approach for evaluation of social...
An approach for evaluation of social...An approach for evaluation of social...
An approach for evaluation of social...
 
Multiview Methodology
Multiview MethodologyMultiview Methodology
Multiview Methodology
 
The Robot Marketeer
The Robot MarketeerThe Robot Marketeer
The Robot Marketeer
 
Tech Essentials - UP Edition
Tech Essentials - UP EditionTech Essentials - UP Edition
Tech Essentials - UP Edition
 

Mehr von Seth Grimes

Recent Advances in Natural Language Processing
Recent Advances in Natural Language ProcessingRecent Advances in Natural Language Processing
Recent Advances in Natural Language ProcessingSeth Grimes
 
Creating an AI Startup: What You Need to Know
Creating an AI Startup: What You Need to KnowCreating an AI Startup: What You Need to Know
Creating an AI Startup: What You Need to KnowSeth Grimes
 
NLP 2020: What Works and What's Next
NLP 2020: What Works and What's NextNLP 2020: What Works and What's Next
NLP 2020: What Works and What's NextSeth Grimes
 
Efficient Deep Learning in Natural Language Processing Production, with Moshe...
Efficient Deep Learning in Natural Language Processing Production, with Moshe...Efficient Deep Learning in Natural Language Processing Production, with Moshe...
Efficient Deep Learning in Natural Language Processing Production, with Moshe...Seth Grimes
 
From Customer Emotions to Actionable Insights, with Peter Dorrington
From Customer Emotions to Actionable Insights, with Peter DorringtonFrom Customer Emotions to Actionable Insights, with Peter Dorrington
From Customer Emotions to Actionable Insights, with Peter DorringtonSeth Grimes
 
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AI
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AIIntro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AI
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
 
Text Analytics Market Trends
Text Analytics Market TrendsText Analytics Market Trends
Text Analytics Market TrendsSeth Grimes
 
Text Analytics for NLPers
Text Analytics for NLPersText Analytics for NLPers
Text Analytics for NLPersSeth Grimes
 
Our FinTech Future – AI’s Opportunities and Challenges?
Our FinTech Future – AI’s Opportunities and Challenges? Our FinTech Future – AI’s Opportunities and Challenges?
Our FinTech Future – AI’s Opportunities and Challenges? Seth Grimes
 
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...Seth Grimes
 
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...Seth Grimes
 
Fairness in Machine Learning and AI
Fairness in Machine Learning and AIFairness in Machine Learning and AI
Fairness in Machine Learning and AISeth Grimes
 
Classification with Memes–Uber case study
Classification with Memes–Uber case studyClassification with Memes–Uber case study
Classification with Memes–Uber case studySeth Grimes
 
Aspect Detection for Sentiment / Emotion Analysis
Aspect Detection for Sentiment / Emotion AnalysisAspect Detection for Sentiment / Emotion Analysis
Aspect Detection for Sentiment / Emotion AnalysisSeth Grimes
 
Content AI: From Potential to Practice
Content AI: From Potential to PracticeContent AI: From Potential to Practice
Content AI: From Potential to PracticeSeth Grimes
 
Text Analytics Market Insights: What's Working and What's Next
Text Analytics Market Insights: What's Working and What's NextText Analytics Market Insights: What's Working and What's Next
Text Analytics Market Insights: What's Working and What's NextSeth Grimes
 
An Industry Perspective on Subjectivity, Sentiment, and Social
An Industry Perspective on Subjectivity, Sentiment, and SocialAn Industry Perspective on Subjectivity, Sentiment, and Social
An Industry Perspective on Subjectivity, Sentiment, and SocialSeth Grimes
 
The Insight Value of Social Sentiment
The Insight Value of Social SentimentThe Insight Value of Social Sentiment
The Insight Value of Social SentimentSeth Grimes
 
Social Data Sentiment Analysis
Social Data Sentiment AnalysisSocial Data Sentiment Analysis
Social Data Sentiment AnalysisSeth Grimes
 

Mehr von Seth Grimes (20)

Recent Advances in Natural Language Processing
Recent Advances in Natural Language ProcessingRecent Advances in Natural Language Processing
Recent Advances in Natural Language Processing
 
Creating an AI Startup: What You Need to Know
Creating an AI Startup: What You Need to KnowCreating an AI Startup: What You Need to Know
Creating an AI Startup: What You Need to Know
 
NLP 2020: What Works and What's Next
NLP 2020: What Works and What's NextNLP 2020: What Works and What's Next
NLP 2020: What Works and What's Next
 
Efficient Deep Learning in Natural Language Processing Production, with Moshe...
Efficient Deep Learning in Natural Language Processing Production, with Moshe...Efficient Deep Learning in Natural Language Processing Production, with Moshe...
Efficient Deep Learning in Natural Language Processing Production, with Moshe...
 
From Customer Emotions to Actionable Insights, with Peter Dorrington
From Customer Emotions to Actionable Insights, with Peter DorringtonFrom Customer Emotions to Actionable Insights, with Peter Dorrington
From Customer Emotions to Actionable Insights, with Peter Dorrington
 
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AI
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AIIntro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AI
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AI
 
Emotion AI
Emotion AIEmotion AI
Emotion AI
 
Text Analytics Market Trends
Text Analytics Market TrendsText Analytics Market Trends
Text Analytics Market Trends
 
Text Analytics for NLPers
Text Analytics for NLPersText Analytics for NLPers
Text Analytics for NLPers
 
Our FinTech Future – AI’s Opportunities and Challenges?
Our FinTech Future – AI’s Opportunities and Challenges? Our FinTech Future – AI’s Opportunities and Challenges?
Our FinTech Future – AI’s Opportunities and Challenges?
 
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
 
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
 
Fairness in Machine Learning and AI
Fairness in Machine Learning and AIFairness in Machine Learning and AI
Fairness in Machine Learning and AI
 
Classification with Memes–Uber case study
Classification with Memes–Uber case studyClassification with Memes–Uber case study
Classification with Memes–Uber case study
 
Aspect Detection for Sentiment / Emotion Analysis
Aspect Detection for Sentiment / Emotion AnalysisAspect Detection for Sentiment / Emotion Analysis
Aspect Detection for Sentiment / Emotion Analysis
 
Content AI: From Potential to Practice
Content AI: From Potential to PracticeContent AI: From Potential to Practice
Content AI: From Potential to Practice
 
Text Analytics Market Insights: What's Working and What's Next
Text Analytics Market Insights: What's Working and What's NextText Analytics Market Insights: What's Working and What's Next
Text Analytics Market Insights: What's Working and What's Next
 
An Industry Perspective on Subjectivity, Sentiment, and Social
An Industry Perspective on Subjectivity, Sentiment, and SocialAn Industry Perspective on Subjectivity, Sentiment, and Social
An Industry Perspective on Subjectivity, Sentiment, and Social
 
The Insight Value of Social Sentiment
The Insight Value of Social SentimentThe Insight Value of Social Sentiment
The Insight Value of Social Sentiment
 
Social Data Sentiment Analysis
Social Data Sentiment AnalysisSocial Data Sentiment Analysis
Social Data Sentiment Analysis
 

Kürzlich hochgeladen

专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改yuu sss
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Seán Kennedy
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhijennyeacort
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectBoston Institute of Analytics
 
毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...
毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...
毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...ttt fff
 
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一z xss
 
SWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptxSWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptxviniciusperissetr
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingsocarem879
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Seán Kennedy
 
Cyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataCyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataTecnoIncentive
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Cathrine Wilhelmsen
 
Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作ys8omjxb
 
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...ssuserf63bd7
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfblazblazml
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsVICTOR MAESTRE RAMIREZ
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queensdataanalyticsqueen03
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 217djon017
 

Kürzlich hochgeladen (20)

专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis Project
 
毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...
毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...
毕业文凭制作#回国入职#diploma#degree美国加州州立大学北岭分校毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#de...
 
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
 
SWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptxSWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptx
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processing
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...
 
Cyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataCyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded data
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)
 
Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作
 
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queens
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
 

Text Analytics Applied (LIDER roadmapping presentation)

  • 1. Text Analytics Applied Seth Grimes Alta Plana Corporation @sethgrimes 2nd LIDER roadmapping workshop – Madrid May 8, 2014
  • 2. Text Analytics Applied 2nd LIDER workshop 2 “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
  • 3. Text Analytics Applied 2nd LIDER workshop 3
  • 4. 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
  • 5. Text Analytics Applied 2nd LIDER workshop 5 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.
  • 6. Text Analytics Applied 2nd LIDER workshop 6 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.
  • 7. Text Analytics Applied 2nd LIDER workshop 7 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.
  • 8. Text Analytics Applied 2nd LIDER workshop 8 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…
  • 10. Text Analytics Applied 2nd LIDER workshop 10 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
  • 11. Text Analytics Applied 2nd LIDER workshop 11 Exploratory analysis, synthesis Decisive Analytics http://www.dac.us/
  • 12. Text Analytics Applied 2nd LIDER workshop 12 http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html A big data analytics architecture (example)
  • 13. Text Analytics Applied 2nd LIDER workshop 13 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.
  • 14. Text Analytics Applied 2nd LIDER workshop 14 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.
  • 15. Text Analytics Applied 2nd LIDER workshop 15 http://altaplana.com/TA2014
  • 16. Text Analytics Applied 2nd LIDER workshop 16 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?
  • 17. Text Analytics Applied 2nd LIDER workshop 17 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.
  • 18. Text Analytics Applied 2nd LIDER workshop 18 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.
  • 19. Text Analytics Applied 2nd LIDER workshop 19 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
  • 20. Text Analytics Applied 2nd LIDER workshop 20 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?
  • 21. Text Analytics Applied 2nd LIDER workshop 21 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
  • 22. Text Analytics Applied 2nd LIDER workshop 22 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...
  • 23. Text Analytics Applied 2nd LIDER workshop 23 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?
  • 24. Text Analytics Applied 2nd LIDER workshop 24 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?
  • 25. Text Analytics Applied 2nd LIDER workshop 25 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…
  • 26. Text Analytics Applied 2nd LIDER workshop 26 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.
  • 27. Text Analytics Applied 2nd LIDER workshop 27 Linked Data Links?
  • 28. Text Analytics Applied Seth Grimes Alta Plana Corporation @sethgrimes 2nd LIDER roadmapping workshop – Madrid May 8, 2014