Entirely tailored sentiment analysis - MeaningCloud webinar

Implement a Totally Tailored

Sentiment Analysis with

MeaningCloud

May 4th, 2016

Webinar

Tailored Sentiment Analysis with MeaningCloud

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…

To learn how to implement

the highest-quality

sentiment analysis

for your application


Contents

What MeaningCloud is and what it is used for

Sentiment analysis: features and limitations

Optimizing your sentiment analysis

• MeaningCloud’s customization tools

Conclusions and Q&A


MeaningCloud: “Meaning as a Service”

Sign up, and use it for FREE at

http://www.meaningcloud.com

http://www.meaningcloud.com/


Text analytics, in the cloud (and on-premises)

Extract meaning and actionable insights from unstructured content

Automation of costly manual activities

MeaningCloud provides this service as a convenient, web-based offering

OpinionsFacts

Concepts

Organizations

People

Semantic

Analysis

Relationships

Themes


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 between 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

8


Customization tools


Sentiment analysis in MeaningCloud

Identify sentiment (positive/negative/neutral or no polarity)

• Document-level (overall)

• Sentence-level

• Associated to mentioned entities/concepts/attributes

The Samsung is more reliable and the iPhone is too expensive

The hotel’s rooms are comfortable, but the restaurant is horrible


Sentiment Analysis API

Assign multilevel polarity to entities and other aspects,

distinguish facts from opinions, and detect irony.

IBM stock fell another 1.51%, while

their cloud business revenue rose 60

percent in 2014.

Aspect Sentiment

IBM - stock N

IBM - revenue P+

Global NEU, DISAGREEMENT,

OBJECTIVE, NON IRONIC

Aspect Sentiment

Excelsior Hotel -

landscapes

P+

Excelsior Hotel - rooms N-

Global NEU, DISAGREEMENT,

SUBJECTIVE, NON IRONIC

5-level polarity (and no polarity) scoring

Aspect-based analysis

Objective (fact) / subjective (opinion)

distinction

Irony detection (beta)

Customizable sentiment models

Excelsior Hotel has the most

amazing landscapes I've ever seen,

but the rooms are disgusting.


Let’s focus on a practical example

Yelp Reviews of Japanese restaurants in London


Limitations of sentiment analysis

Attributes relevant for the domain are not detected, e.g.:

• For Japanese restaurants we need to know opinions about

• Dishes: sushi, sashimi, ramen, etc.

• Quality characteristics: price, atmosphere, etc.

An expression’s polarity depends on domain and context, e.g.:

• “Cheap” is positive… unless you’re talking about luxury products.

• The sentence “The highest interest rate in the industry!” is

positive in the domain of savings but negative in the domain of

mortgages.


How we can optimize sentiment analysis

By including attributes that are relevant to the domain and

focusing the analysis around them

Personal dictionary of entities and concepts

By specifying the polarity of expressions in the domain

depending on the context

Personal sentiment model


Personal dictionary of entities and concepts

Restaurant

Dish

Ramen Sushi Sashimi etc.

Quality

Price Staff Atmosphere etc.


Personal sentiment model

Polarity of expressions

to share POLARITY = NONE

Polarity depending on context

portion|slice

TOGETHER IN THE SAME SENTENCE WITH small|tiny|meager

POLARITY = N

Polarity depending on context and function

service

TOGETHER IN THE SAME SENTENCE WITH slow

AND ACTING AS <noun> POLARITY = N


Conclusion

The highest-quality sentiment analysis,

at your fingertips

Attribute-level analysis

Personal dictionaries, to focus the analysis on

aspects of interest

Personal sentiment models to adjust polarity

depending on the domain


Democratizing the extraction of meaning

High quality semantic analysis

Optimized technology mix

Continuously updates semantic resources

High-level APIs, e.g., User Profiling

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

OpinionesTemasHechos

Conceptos

Organizaciones

Personas

Relaciones


Q & A


Stay tuned to our emails and blog

We’ll be posting a recording of the webinar and

its contents (data and models) 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/

Technology

Entirely tailored sentiment analysis - MeaningCloud webinar