SAS Text Analytics help organizations address 
many big data issues that arise from unstructured 
content. By applying linguistic rules and statistical 
methods to automatically assess, analyze and 
act upon the insight buried in text. Such as social 
media content, call center logs, survey data, 
email, loan applications, service notes, and 
insurance or warranty claims. This short demo 
will focus on a call center from a credit card 
company and will illustrate high level text 
analytical techniques used by SAS Text Miner to 
analyze the voice of the customer. 
Some companies start their unstructured data 
analysis by having people read verbatims but this 
simply isn’t a scalable solution and has a number 
of challenges. We advocate a more automated 
approach to discovery with SAS Text Miner. 
Using interactive text filtering we’re presented 
with the raw data at the top of the screen and a 
terms list down below. The terms list shows the 
high frequency, high value terms that appear in 
the document collection. Stemming is indicated by 
the plus sign and is used to consolidate verbs to 
their root form and nouns to their singular form. 
This helps eliminate clutter. Users can define 
synonyms by simply clicking the terms they’d like 
to treat as synonyms, such as card and credit 
card, right mouse clicking and choosing treat as 
synonyms. We have to choose the parent. In this 
case we’re going to choose credit card. Now if 
we stem, we can see both card and credit card 
and their forms listed in the list.
Exploring the terms list, we see words like 
payment, activate, rate and fee. These are all 
facets that we’ll analyze downstream to 
determine sentiment or tonality. Are these 
categories causing pockets of customer 
dissatisfaction? If we further explore a word like 
fee using concept linking, we see words that are 
associated with fee. Membership fee, annual, late 
fee, and limit fee. Words don’t exist in a vacuum. 
They exist with context. Concept linking helps 
you determine what that context is. We can 
further explore by simply double clicking. We can 
use this information to build a small taxonomy that 
can aid us in identifying concentrations of chatter 
in these areas. This can give us information about 
how wide spread the issues are and track 
emerging issues over time. Armed with insights 
from Text Miner, an analyst can begin
building a sentiment analysis in  
SAS Sentiment Analysis Studio.
Sentiment analysis gives analysts the 
power to assess tonality or derive the emotion 
from text using statistical, rules based or hybrid 
approaches. In a testing phase, positive tonal key 
words are highlighted in green, negative tonal 
keywords are highlighted in red, and products 
and features noted are highlighted in blue. This 
example shows quite a bit of negative emotion 
regarding being charged with late and annual 
fees. SAS Sentiment Analysis is not a black box, 
a report detailing a list of featured definitions and 
rules is provided. This helps with streamlining and 
refining the analysis. SAS Text Analytics is part 
of the SAS Business Analytics Framework, 
which means results can be surfaced using the 
dashboard view showing an overview of the 
sentiment by feature. It’s apparent from the 
dashboard that there’s quite a bit of negative 
sentiment around fees but specifically which 
fees? SAS Content Categorization can be used 
as a final step, drilling into details about features, 
such as fees, to discover which areas might 
need improvement or might be driving  
customer dissatisfaction. 
SAS Text Analytics is a suite of tools designed to 
help organizations explore, analyze and organize 
unstructured data, identify the needles in a 
haystack and provide organizations with 
actionable intelligence. Want to learn more about 
SAS Text Analytics? Visit us online. You might 
want to check out some related videos on 
data mining, text analytics and forecasting.
