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Better Text Analytics with
MeaningCloud
How our customization tools can boost text
analysis accuracy
Webinar - Daedalus / MeaningCloud, May 14, 2015
Introduction
Presenter
Logistics
 Send text questions, or
 “Raise your hand” to speak and we’ll open your mic
 Will publish link to recorded webinar
Jarred McGinnis, PhD
Business Development, UK
Agenda
 Text analytics: accuracy, precision, recall
 Customized linguistic resources for improved accuracy
 MeaningCloud customization tools
 Conclusions, Q&A
Text analytics
Extract meaning and actionable insights from unstructured content
Automatization of costly manual activities
Opinions
Facts
Concepts
Organizations
People
Semantic
Analysis
Relationships
Themes
Just how precise is precise?
Precision is relative
 Even experts aren’t 100% precise
 Tests involving human analysts: 85-95% agreement
 Along with precision, recall is also important
High precision
High recall
High precision
Low recall
Low precision
High recall
Identified by algorithm
Accuracy: precision & recall
 Precision and recall are inversely related
 Trade-off needed
 Requirements are application-specific
 Brand monitoring in social media: high precision, low recall
 Counter-terrorism : high recall, low precision
Precision – Recall Curve
State of the Art for Text Analysis
Precision Measurements
 Topic Extraction: 70-85%
 Classification: 70-80%
 Sentiment Analysis: 60-70%
Quality improvement depends on the adaptation of the tools and
resources to the application / task
MeaningCloud: cloud-based semantic APIs
Register and use it FREE at http://www.meaningcloud.com
APIs services of MeaningCloud Sentiment analysis
 Global
 Aspect-based
Classification
 Standard models
Topic extraction
 Entities
 Concepts
 Dates
 Addresses
 Economic quantities
 Time expressions
 …
https://www.meaningcloud.com/demos/media-analysis/
MeaningCloud: standard resources
Ontodaedalus (ontology)
 437 nodes
 78 themes
 250,000+ lemmas/language
https://www.meaningcloud.com/developer/
documentation/ontodaedalus
MeaningCloud: Standard classification models
‘out-of-the-box’ support
of well-known
classification standards
 IPTC: news
 Business Reputation:
corporate reputation
 EuroVoc: public
administration
 IAB (coming soon):
advertisinghttps://www.meaningcloud.com/developer/resources/models
A practical example
A walk through MeaningCloud customization tools
VoC / Customer Insights scenario
Social networks, forums
Survey verbatims
encuestas
Contact Center interactions:
voice, email…
Structure and
extract meaning
What companies/
brands are they mentioning?
What are they
talking about?
What’s their opinion?
Analysis
Insights
Opinions
The sentence “The
highest interest rate in
industry!” is…
 Positive, if talking
about savings
 Negative, if talking
about mortgages
Customized linguistic resources improve
accuracy
Mentions
 Names of banks and
financial companies, e.g.,
Citibank, BBVA
 Product names, e.g., Your
Waysm Account. Compass
Account…
Themes
Example: analysis of a bank’s customer opinions
Products
Accounts
Checkin
g
Savings
Borrowing
Credit
Mortgage
Channel
Office
Phone
Internet
Demo agenda
 Bank dictionary
 Bank names, product names (entities)
 Generic product names, e.g., mortgage (concepts)
 Classification models
 Channel model: phone, web…
 Sentiment models (preview)
MeaningCloud customization tools
Customized dictionaries
Creating a new dictionary
Possible to import
dictionary from file
Creating a new entity
Aliases: It is NOT
necessary to explicitly
include “trivial”
aliases as the engine
generates typical
variants
Use your own
ontology
Possible to include
additional semantic
info
Resulting dictionary
 Entities
 Concepts
Dictionary derived ontology
Dictionary import
The best way to include a pre-existing dictionary in MeaningCloud
Form Alias ID Semantic info attributes
Outcome: APIs identify topics in dictionary
Identifies semantic info
Product: Cash Card Account
Type: Currents Accounts
Bank: Barclays
Custom classification models
Creating a new model
Ability to import
model from file
Defining a new category:
hybrid approach
Rule-based
Training-based
Possible to opt for one of
the approaches, or to
combine both, depending on
the application
Defining a category: training
 Fed with precodified training texts
 Based on machine learning technology
Defining a category: rules
Terms that
 Are indispensable
 Are banned
 Increase relevance
 Reduce relevance
Improving precision and recall using rules and
training
Statistical Rules Hybrid
Benefits  Fast, provided tagged
texts are available
 Good accuracy for long
texts
 No false positives
 Very good accuracy for
limited environments
 Can be easily started with
training texts
 Does not need exhaustive
definition of rules
Dis-
advantages
 “Black box” approach
 False positives difficult
to correct
 Bias in results,
depending on training
 Costly if starting from
zero
 False negatives,
depending on rule
quality
 Difficult to scale
 Requires deep domain
knowledge
Resulting model
Classification model import
The best way to configure a pre-
existing model in MeaningCloud
Outcome: APIs classify according to model
Justifies classification relevance
depending on the terms
appearing
Application of Sentiment Analysis
Sentiment: use of custom entity and concept
dictionaries
Polarity associated with Barclays
Custom sentiment dictionaries (COMING SOON)
 Not all terms have the same polarity in all domains
 E.g., in the luxury goods’ domain the term “cheap” doesn’t necessarily have a positive
polarity (like in other domains)
 Define a luxury goods custom sentiment dictionary where: “cheap”  N
 A given term can have different polarities, according to context
We’re presently testing this feature. If you want to take part in the private beta send an
email to support@meaningcloud.com
Term Context Polarity
close stock market NEUTRAL
close deal, contract Pos
close company Neg
Conclusions
How to improve accuracy?
 Graphical tools
 Possibility to include own dictionaries and models
 Broad coverage: mentions, themes, opinions…
 Empowered users
High accuracy analysis is within your reach.
Democratizing the extraction of meaning
High quality semantic
analysis
 Optimized technology mix
 Continuously updates semantic
resources
 High-level APIs, e.g., Corporate
Reputation
 Customizable to customer
domain: models, dictionaries,
sentiment
Affordable, no risks
 Mature, tested technology
 Test and use for FREE
(40,000 requests per
month)
 Pay per use
 No commitment or
permanence
 Commercial plans beginning
at $99 /mo
For developers and non
technical users
 Add-in for Excel
 Standard web services
APIs
 Plug-ins and SDKs for
diverse environments and
languages
 Plug-and-play approach
OpinionesTemas
Hechos
Conceptos
Organizaciones
Personas
Relaciones
Thank you for your attention!
Questions, suggestions...
Jarred McGinnis, PhD
Business Development, UK
jarred@daedalus.es
http://www.meaningcloud.com
http://www.daedalus.es

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Boost Your Text Analytics Accuracy - MeaningCloud Webinar

  • 1. Better Text Analytics with MeaningCloud How our customization tools can boost text analysis accuracy Webinar - Daedalus / MeaningCloud, May 14, 2015
  • 2. Introduction Presenter Logistics  Send text questions, or  “Raise your hand” to speak and we’ll open your mic  Will publish link to recorded webinar Jarred McGinnis, PhD Business Development, UK
  • 3. Agenda  Text analytics: accuracy, precision, recall  Customized linguistic resources for improved accuracy  MeaningCloud customization tools  Conclusions, Q&A
  • 4. Text analytics Extract meaning and actionable insights from unstructured content Automatization of costly manual activities Opinions Facts Concepts Organizations People Semantic Analysis Relationships Themes
  • 5. Just how precise is precise? Precision is relative  Even experts aren’t 100% precise  Tests involving human analysts: 85-95% agreement  Along with precision, recall is also important High precision High recall High precision Low recall Low precision High recall Identified by algorithm
  • 6. Accuracy: precision & recall  Precision and recall are inversely related  Trade-off needed  Requirements are application-specific  Brand monitoring in social media: high precision, low recall  Counter-terrorism : high recall, low precision Precision – Recall Curve
  • 7. State of the Art for Text Analysis Precision Measurements  Topic Extraction: 70-85%  Classification: 70-80%  Sentiment Analysis: 60-70% Quality improvement depends on the adaptation of the tools and resources to the application / task
  • 8. MeaningCloud: cloud-based semantic APIs Register and use it FREE at http://www.meaningcloud.com
  • 9. APIs services of MeaningCloud Sentiment analysis  Global  Aspect-based Classification  Standard models Topic extraction  Entities  Concepts  Dates  Addresses  Economic quantities  Time expressions  … https://www.meaningcloud.com/demos/media-analysis/
  • 10. MeaningCloud: standard resources Ontodaedalus (ontology)  437 nodes  78 themes  250,000+ lemmas/language https://www.meaningcloud.com/developer/ documentation/ontodaedalus
  • 11. MeaningCloud: Standard classification models ‘out-of-the-box’ support of well-known classification standards  IPTC: news  Business Reputation: corporate reputation  EuroVoc: public administration  IAB (coming soon): advertisinghttps://www.meaningcloud.com/developer/resources/models
  • 12. A practical example A walk through MeaningCloud customization tools
  • 13. VoC / Customer Insights scenario Social networks, forums Survey verbatims encuestas Contact Center interactions: voice, email… Structure and extract meaning What companies/ brands are they mentioning? What are they talking about? What’s their opinion? Analysis Insights
  • 14. Opinions The sentence “The highest interest rate in industry!” is…  Positive, if talking about savings  Negative, if talking about mortgages Customized linguistic resources improve accuracy Mentions  Names of banks and financial companies, e.g., Citibank, BBVA  Product names, e.g., Your Waysm Account. Compass Account… Themes Example: analysis of a bank’s customer opinions Products Accounts Checkin g Savings Borrowing Credit Mortgage Channel Office Phone Internet
  • 15. Demo agenda  Bank dictionary  Bank names, product names (entities)  Generic product names, e.g., mortgage (concepts)  Classification models  Channel model: phone, web…  Sentiment models (preview)
  • 18. Creating a new dictionary Possible to import dictionary from file
  • 19. Creating a new entity Aliases: It is NOT necessary to explicitly include “trivial” aliases as the engine generates typical variants Use your own ontology Possible to include additional semantic info
  • 22. Dictionary import The best way to include a pre-existing dictionary in MeaningCloud Form Alias ID Semantic info attributes
  • 23. Outcome: APIs identify topics in dictionary Identifies semantic info Product: Cash Card Account Type: Currents Accounts Bank: Barclays
  • 25. Creating a new model Ability to import model from file
  • 26. Defining a new category: hybrid approach Rule-based Training-based Possible to opt for one of the approaches, or to combine both, depending on the application
  • 27. Defining a category: training  Fed with precodified training texts  Based on machine learning technology
  • 28. Defining a category: rules Terms that  Are indispensable  Are banned  Increase relevance  Reduce relevance
  • 29. Improving precision and recall using rules and training Statistical Rules Hybrid Benefits  Fast, provided tagged texts are available  Good accuracy for long texts  No false positives  Very good accuracy for limited environments  Can be easily started with training texts  Does not need exhaustive definition of rules Dis- advantages  “Black box” approach  False positives difficult to correct  Bias in results, depending on training  Costly if starting from zero  False negatives, depending on rule quality  Difficult to scale  Requires deep domain knowledge
  • 31. Classification model import The best way to configure a pre- existing model in MeaningCloud
  • 32. Outcome: APIs classify according to model Justifies classification relevance depending on the terms appearing
  • 34. Sentiment: use of custom entity and concept dictionaries Polarity associated with Barclays
  • 35. Custom sentiment dictionaries (COMING SOON)  Not all terms have the same polarity in all domains  E.g., in the luxury goods’ domain the term “cheap” doesn’t necessarily have a positive polarity (like in other domains)  Define a luxury goods custom sentiment dictionary where: “cheap”  N  A given term can have different polarities, according to context We’re presently testing this feature. If you want to take part in the private beta send an email to support@meaningcloud.com Term Context Polarity close stock market NEUTRAL close deal, contract Pos close company Neg
  • 36. Conclusions How to improve accuracy?  Graphical tools  Possibility to include own dictionaries and models  Broad coverage: mentions, themes, opinions…  Empowered users High accuracy analysis is within your reach.
  • 37. Democratizing the extraction of meaning High quality semantic analysis  Optimized technology mix  Continuously updates semantic resources  High-level APIs, e.g., Corporate Reputation  Customizable to customer domain: models, dictionaries, sentiment Affordable, no risks  Mature, tested technology  Test and use for FREE (40,000 requests per month)  Pay per use  No commitment or permanence  Commercial plans beginning at $99 /mo For developers and non technical users  Add-in for Excel  Standard web services APIs  Plug-ins and SDKs for diverse environments and languages  Plug-and-play approach OpinionesTemas Hechos Conceptos Organizaciones Personas Relaciones
  • 38. Thank you for your attention! Questions, suggestions... Jarred McGinnis, PhD Business Development, UK jarred@daedalus.es http://www.meaningcloud.com http://www.daedalus.es