I'm Mary Beth Ainsworth, the Global Product Marketing
Manager for Text Analytics.
And joining me today is Simran Bagga,
the Principal Product Manager for Text Analytics here at SAS.
Simran, thanks for speaking with us today.
SIMRAN BAGGA: Glad to be here.
MARY BETH AINSWORTH: Many people may not
realize that text is the largest human-generated data source,
and it grows exponentially on a daily basis.
Just think about all of the emails and text messages,
social media postings, chats, and even online reviews
that we create everyday.
SIMRAN BAGGA: That's right.
Organizations are generating and storing
large amounts of data, majority of which
is unstructured content.
To truly unlock the value, uncover emerging trends,
and spot new opportunities for action,
there's really only one solution, text analytics.
MARY BETH AINSWORTH: Today, Simran
is going to show us SAS Visual Text Analytics, which
is a comprehensive text-analytics solution that
combines natural-language processing, machine
learning, and linguistic rules.
SIMRAN BAGGA: Visual Text Analytics
provides an end-to-end analytics framework enabling users
to prepare data, dynamically explore and visualize
text, as well as build and deploy
a variety of text-analytics models.
Let's take a look at Visual Text Analytics in action.
In this specific use case, I'm working
with text reviews from apartment rentals
where customers have provided their feedback, likes
and dislikes, and also a rating score related to the stay.
My goal is to quickly uncover insights and commonly occurring
themes, understand associated sentiment,
and then build a categorization model that
classifies future reviews.
In the Data Preparation window, I can transform or subset
my data.
Here I only want to evaluate the experience
of rentals that cost over $200.
After preparing my data, I can choose
to build text models directly, but here
I first want to explore the text before I build the models.
I'm using the text topics exploration.
Here I'm asking text topics to be generated
from the comments field, along with the sentiment information.
Topics provide more context than just terms alone.
So I can see without having to read thousands of documents
that customers are talking about walking distance
from restaurants, feeling like home,
or maybe unhappy about automatic cancellation of reservations.
Text topics are discovered from your data
using natural-language processing, or NLP, and machine
learning.
NLP output feeds into machine learning
to identify these themes.
MARY BETH AINSWORTH: Simran, NLP is the most foundational aspect
of text analytics.
Can you highlight some NLP capabilities
and show how users can modify topics
for more-relevant results?
SIMRAN BAGGA: Absolutely.
NLP essentially helps the machine understand
the concept of a language.
And in Visual Text Analytics we provide native support
for 30 languages.
SAS's NLP capabilities include text parsing
and contextual extraction.
Entities, noun groups, as well as relationships and facts
relevant to the business is valuable output from NLP
that feeds into topic analysis and categorization models.
I've created a text analytics project
where I can enhance the output from NLP
to generate relevant topics in categorization models.
Inside the project, I can see data attributes
and the best-practice pipeline that ships with Visual Text
Analytics.
The two nodes following the data node
give the user more control over NLP output.
I can choose to include predefined concepts.
These predefined concepts are standardized
across all languages.
I can also add my own custom concepts
that allow me to extract business-specific information.
For the purpose of this analysis,
I want to extract any type of room amenities or hotel
amenities that my customers have mentioned in their reviews.
In the text parsing node, I can see all the terms
recommended to keep and drop.
The user has complete control over what terms
they want to keep for analysis.
I can select a term like location
and see other terms that are associated with it in the term
map.
Here we see that location is associated with the term
great location, good location, and also oftentimes it's
referred to with the word perfect.
The term great also appears very frequently
in my document collection.
Clicking on show similarity scores
will return a sorted list of terms
that are similar to great.
This can be useful in creation of synonyms
or linguistic rules.
MARY BETH AINSWORTH: These automated insights
generated by NLP can significantly
reduce the manual effort that goes into reading
and understanding text.
The user doesn't need to have a preconceived notion of what
they're looking for.
SIMRAN BAGGA: Right.
Visual Text Analytics provides capabilities
to leverage machine learning, enable automation,
and reduce time to solution.
For example, Visual Text Analytics
provides a domain-independent sentiment model
that displays sentiment scores in the user interface.
This can be a helpful aid for users
trying to evaluate topics or build concept or categorization
rules.
This allows users to see relevance of sentiment scores.
And users can refine parameters to make
the resulting topic set even more meaningful and useful.
You can select a topic to see documents and terms that
match that topic.
You can also merge topics that are similar in nature.
For example, here I see walk, few minutes, common station,
and walking distance to restaurants.
Both these topics talk about walking distance to something.
As a user, I want to merge these topics
and create one combined topic.
You can also select a single term or a set of terms
and then request a new topic to be created.
And you can also select any topics of interest
and promote them to categories.
Here I'm selecting two positive topics, "great apartment stay
place" and "walking distance from restaurants", and two
negative topics, one that talks about noise and second
that talks about automatic cancellation of reservations,
and promote these topics to my categories.
This will allow the user to capture future reviews that
represent these topics.
Opening the category node shows me these promoted topics
and the rules that it has automatically
generated that I can further operationalize.
I can also rearrange these topics in my taxonomy
as I see fit.
As you can see, I've created two broad categories
for positive aspects and negative aspects.
And I have captured "great apartment walking distance"
as well as automatic cancellation and noise problems
into this taxonomy.
Visual Text Analytics enables collaboration among users
by allowing them to save their best practice nodes
and pipelines in the toolbox that can be reused or shared
with others.
MARY BETH AINSWORTH: Let's talk about deployment.
Now that these text models are built,
users have a few options to operationalize them.
Why don't you tell us about these options?
SIMRAN BAGGA: Sure.
Model deployment can vary based on the use case.
And Visual Text Analytics provides
the much-needed flexibility in deployment options.
Users may simply want to visualize the output of a text
model, use the text analytics output as input
into other advanced analytic techniques,
or deploy the analysis into an operational system.
Here I have an example of a report that
uses the output from a categorization model,
enabling decision makers to visually understand
key areas of concern.
They can also analyze and visualize
relevant factors that lead to specific outcomes of interest.
In this example, I have a decision tree
that was generated using a combination of structured data
and topics derived from unstructured text, which
gives me quick insights into factors
that contribute to review ratings given by customers.
Visual Text Analytics also enables model deployment
in Hadoop, in batch, and in stream
to support edge analytics.
The goal is to reduce time to action
and minimize data movement.
MARY BETH AINSWORTH: Simran, thanks
so much for sharing SAS Visual Text Analytics with us today.
SIMRAN BAGGA: My pleasure.
MARY BETH AINSWORTH: Here at SAS we
say that data without analytics is potential
yet to be realized.
If you have text data, the power of running text analytics
and gaining insights on that data is incredible.
Thanks for watching.