When to use the different text analytics tools - Meaning Cloud

Text Analytics Tools:

When and How to

Use Them

February 8th, 2017

Webinar

Text Analytics Tools

Before we get started…

Presenter

How to participate

• Send questions with the chat feature, or

• Click the “Raise your hand” button to speak

and we’ll enable your mic

• Afterwards, you’ll be able to access a recording of the

webinar and its contents as tutorials on our blog

Antonio Matarranz

CMO


The purpose of this webinar…

Learn what the main Text

Analytics functions are and

what they can do for us


Agenda

Introduction to text analytics

Application scenarios. Benefits and challenges

Text analytics functions. Description and use cases

Quality of text analytics tools

A look at MeaningCloud’s roadmap

Conclusions and Q&A


Why should we be using text analytics?

Structured data

Unstructured

content


OpinionsFacts

Concepts

Organizations

People

Semantic

Analysis

Relationships

Themes

Text analytics

Extract meaning and actionable insights from unstructured content

Automation of costly manual activities


Text analytics functions

Information

extraction, NER

Categorization

Clustering

Sentiment analysis

Morphosyntactic

analysis

…


APPLICATION SCENARIOS


Social media analysis

Management of user generated content

Security & defense

Challenge: informal language

Understand the conversation in social networks, blogs, forums…

Brand and reputation monitoring

Signals, customer journey, intent, social leads

User profiling


Voice of the Customer (VoC) / Customer Experience

Extend your view of the customer

to new, non traditional data

sources: comments in surveys,

contact center interactions, social

conversations…

Demographic data

CRM / Mktng.automation

Contact Center interactions

Devices

Product use

Navigation

Social

360º vision

Orders and Payments

Unsolicited, unstructured sources

contribute to create integrated

360º customer view

Integrated customer view helps

provide personalized, consistent,

context-specific and relevant

experiences


Voice of the Citizen/Voter

Analysis of social opinions and segmentation allow to understand

citizen attitudes and behaviors

Citizen profiling. Opinions and trends about political situation, government and their services

Emergency detection and lifecycle management


Voice of the Employee / People Analytics

LeadersRegular

ArmyGeeks

Improve workforce understanding

Analysis of surveys, performance reviews, exit interviews, CVs, communications

Attitudes/skills/behaviors most present among top performers

Effective talent management and employee retention


Semantic analysis of content for enhanced exploitation and relation

Better understanding and use of archive. Generation of high-value content

Improved audience engagement thanks to personalization, recommendation and topical

contents

New ways of monetization: targeted advertising, distribution and syndication

Moderation and understanding of user generated content

Intelligent content (media, publishers)


For knowledge-intensive industries and departments

Leverage the tacit knowledge hidden in your document repositories

Semantic tagging and analysis of documents for advanced retrieval and exploitation

Knowledge management


E-discovery and regulatory compliance

Analysis of electronic documents and communications to discover evidence

Legal proceedings, regulated industries (e.g., financial services)

Sources: documents, phone call transcriptions, email, chat, social…

Low latency enables criminal behavior prevention and quick response


TEXT ANALYTICS FUNCTIONS


MeaningCloud: “Meaning as a Service”

(SaaS and on-premises)

Sign up, and use it for FREE at

http://www.meaningcloud.com

http://www.meaningcloud.com/


MeaningCloud’s APIs

Identifies occurrences of

names of people,

organizations, abstract

concepts, quantities, etc.

Theme classification

according to

predefined taxonomies

Identifies general and

attribute-level polarity

Distinguishes among 60

languages

Detailed morphosyntactic analysis Evaluates the impact of

opinions on several

reputational axes

Discover meaningful topics and

similarities among texts without

relying on predefined

taxonomies


Add-in for Excel

An experience fully integrated into Excel

Easy to use - No programming!

The most convenient way to evaluate, prototype, and use MeaningCloud

19


Topic Extraction API

Disambiguate appearances of brands, companies, organizations, people,

concepts… and many more

Contextual disambiguation

• Apple = company (not fruit)

Coreference

Based on standard ontology

Extendable/customizable dictionaries

In a filing with the SEC today, Apple revealed that CEO Tim Cook has donated the equivalent to approximately $6.5 million in Apple stock shares to charity this week. Since becoming CEO in 2011, Cook has promoted charity as a key part of Apple’s mission. Upon taking over, Cook initiated an employee charity program. Apple has also expanded its offerings for employees to help their communities.

Topic

detected

Semantic information

Tim Cook Person, Timothy Donald Cook,

Executive Apple Inc.

Apple Company, Apple Inc., Technology, USA

SEC Organization, Securities and Exchange

Comission, Government, USA

$6.5 million Monetary amount, USD, 6.5 million

charity Concept, charity


MeaningCloud: standard ontology

Built-in ontology

437 nodes

78 themes

250,000+ lemmas/language

Continuously updated

https://www.meaningcloud.com/developer/

documentation/ontology

https://www.meaningcloud.com/developer/documentation/ontology

https://www.meaningcloud.com/developer/documentation/ontology


What is topic extraction for?

Sophisticated detection of appearances/mentions of brands, people,

companies, concepts…

• Context-aware disambiguation

• Considering variants

• Coreference

Application examples:

• Key word extraction

• Document annotation: news, books, emails, records

• Social media monitoring

• Voice of the Customer / Employee / Citizen / Patient analysis

• User profiling (interests)


Text Classification API (featuring standard models, e.g. IAB)

Mix machine learning and rules to accurately classify text according to

predefined categories

The World Cup is the best way to see the

potential football can have for your inbound

travel, economic success and positive public

image:

The 2006 World Cup in Germany was a prime

example of this power with: $200+ per day

average tourist spending, 50,000 new jobs

created, 18 million people at Fan-Fests, total

worldwide TV viewership at 30 billion and 4.2

billion official webpage views. In a survey, 90%

of foreigners who visited the World Cup said

they felt welcome there and would recommend

Germany as a holiday destination. "The World

Cup marks an enormous gain in Germany's

image, even if it's difficult to put an economic

figure on this change in image, the economy as

a whole will certainly benefit from it." the

German economics minister, Michael Glos,

said.

Categories Relevance

Sports – World soccer 0.7

Travel - Europe 0.2

Arts & Entertainment - Television 0.3

IAB (English)

Hybrid technology

• Machine learning and/or rules

Features standard classification models

• IPTC (news), IAB (advertising), EuroVoc

(public administration), Social Media,

Business Reputation

Customizable classification models


MeaningCloud: standard classification models

‘Out-of-the-box’ support of

well-known predefined

classification standards

IPTC: news

IAB: targeted advertising

EuroVoc: public

administration

Social Media: social

conversations

… and more to come

https://www.meaningcloud.com/developer/documentation/supported-models





Classification technologies

Classifiers use patterns/vectors that represent each category

Technologies to generate those representations

• Statistic

• Rule-based

Training

documents

for category

Machine

learning

Rules for

category

Rule

codifier

Rule 1

Rule 2

Rule 3

Rule 4

Category

representation

Category

representation


What is text classification for?

Theme categorization: category is inferred from whole content

• Text is similar to others belonging to the category

• Text verifies certain rules

• In general it is not necessary that

certain term explicitly appears


• Document annotation: news, books, emails, records


• Conversation analysis in social media

• User profiling (interests)


Text Clustering API

Group similar texts and discover meaningful themes

27

Financial crisis

Greenhouse effect

No predefined taxonomy required

(unsupervised learning)

Text-specific processing

Text grouping based on

• Adherence to a theme

• Content similarity

Cluster title Size Score Document list

Financial

crisis

4 0.96 Doc1, Doc4, Doc7,

Doc8

Greenhouse

effect

5 0.34 Doc2, Doc3, Doc5,

Doc6, Doc9


What is text clustering for?

Grouping of similar texts and discovery of meaningful themes

• Without relying on predefined taxonomies


• Duplicate detection

• Discovery of structure in document collections

• Discovery of conversation themes in social media

• Discovery of the "new voice" of Customer /

Employee / Citizen / Patient


Sentiment Analysis API

Assign multilevel polarity to entities and other aspects, discriminate facts

from opinions and detect irony

Aspect Sentiment

Excelsior Hotel - landscapes P+

Excelsior Hotel - rooms N-

General NEU, DISAGREEMENT,

SUBJECTIVE, NON IRONIC

5-level polarity (plus absence of polarity) scoring

Aspect-based analysis

Objective (fact) / subjective (opinion) discrimination

Irony detection (beta)

Customizable sentiment models

Excelsior Hotel has the most

amazing landscapes I've ever seen,

but the rooms are disgusting.


What is sentiment analysis for?

Opinion analysis and mining (polarity)

• General and at attribute/aspect level

• Fact/opinion discrimination


• Social media monitoring



Lemmatization, PoS and Parsing API

Detailed morphosyntactic and semantic analysis

Syntactic analysis

Lemmatization

Part of Speech tagging

Relationships

Quotations

Topics: entities, concepts, etc.

Sentiment analysis


What is morphosyntactic analysis for?

Analysis of a text‘s deep structure

• Morphological, grammatical, semantic


• Text proofreading: spell,

grammar and style

• Support for the detection of

semantic relationships, e.g.,

“CompanyX has invested in

CompanyY”

• In MeaningCloud’s case,

applications of Topics

Extraction and Sentiment Analysis

Use it for FREE at www.mystilus.com

http://www.mystilus.com/


User Profiling API

Use the profile and content generated by the user to infer his demographic

& psychographic attributes

20% of companies say process digitization

yields actionable #analytics

Is your IT team talking SMAC (#social,

#mobile, #analytics, & #cloud)?

Five Rules of Modern Icon Design

http://bit.ly/1y3B6i6

What Twitter Can Be.

http://wp.me/p2Gq8C-6E Just if they'd play

nice with the ecosystem ... #socialtv

#recommendation

What your name says about your age,

where you live, your politics & your job

http://wapo.st/1RkqDcA

Londoner, hooked on data science, NLP

and REST.

Social posts

Social profile

Atribute Value

Person/Organization Person

Gender Male

Age 25-35

Location London

Occupation Engineer

Brands IBM

Demographic

Person /organization

Gender

Age

Location

Occupation

Psychographic

Affinities

Lifestyle…


What is user profiling for?

Demographic and psychographic profiling of

users


• Audience/Market understanding and

segmentation

• Community analysis in social media

• Influencer marketing


IS THIS ALL A QUESTION OF

PRECISION?


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


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., JPMorgan, BNP

Paribas, Citibank

Product names, e.g.,

Your Way Account.

Compass Account…

Themes

Example: analysis of a bank’s customer opinions

Products

Accounts

Checking

Savings

Borrowing

Credit

Mortgage

Channel

Office

Phone

Internet


MeaningCloud customization tools


Customization tools

Create your own dictionaries, classification

models, and sentiment analysis

Graphical user interface - no programming!

Improve precision & recall

Learn more about customization in this webinar

https://www.meaningcloud.com/blog/customize-your-text-analytics-tools-recorded-webinar


A vew into the future

MeaningCloud’s roadmap

Extension for RapidMiner: combine data and text analytics

New languages: Russian, Chinese, Arabic… and many more

New APIs: Summarization, Parts of Document

Vertical Packs: VoC (general and several industries), VoE, Health

Insight Extractor: a granular categorizer and information extractor based on

semantic rules

Q1 2027 Q2 2017 Q3 2017 Q4 2017 Q1 2018

Extension for

RapidMiner Insight Extractor

Aditional languages

Summarization API,

Parts of Document API Vertical Packs


In conclusion

Tools that turn text into

insights Countless applications

Accuracy = customization MeaningCloud: specialists in

text analytics


Q & A


Stay tuned to our emails and blog

We’ll be posting a recording of the webinar and

its contents as tutorials soon


Thank you for your attention!

Questions, suggestions...

Antonio Matarranz

CMO

[email protected]

http://www.meaningcloud.com

mailto:[email protected]

http://www.meaningcloud.com/