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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
Tailored Sentiment Analysis with MeaningCloud
The purpose of this webinar…
To learn how to implement
the highest-quality
sentiment analysis
for your application
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
MeaningCloud: “Meaning as a Service”
Sign up, and use it for FREE at
http://www.meaningcloud.com
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
Customization tools
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
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.
Tailored Sentiment Analysis with MeaningCloud
Let’s focus on a practical example
Yelp Reviews of Japanese restaurants in London
Tailored Sentiment Analysis with MeaningCloud
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.
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
Personal dictionary of entities and concepts
Restaurant
Dish
Ramen Sushi Sashimi etc.
Quality
Price Staff Atmosphere etc.
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
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
Tailored Sentiment Analysis with MeaningCloud
Q & A
Tailored Sentiment Analysis with MeaningCloud
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!
Tailored Sentiment Analysis with MeaningCloud
Thank you for your attention!
Questions, suggestions...
Antonio Matarranz
CMO
http://www.meaningcloud.com