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starting to use sentiment analysis technology and artifcial intelligence to determine how people really eel about them

By Sean Sposito

Photos by Chris Covatta

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Those robots, really snippets of code dipping

and diving into paragraphs and sentences across

the internet, gave the bank what focus groups and

pollsters would have taken months to conrm.

The reports the bank receives from the

machines give BBVA daily insights into consumer

reaction to the bank and its competitors.

We look at our own [bank]. We look at ourmajor competitors, says John Wessman, BBVA

Compass executive vice president and chief mar-

keting ocer. You tend to get a little bit dierent

perspective from consumers. They will be much

more open and sharing on their Facebook accounts

than they would be if they called a call center.

The technoloy BBVA used is an outgrowth

of sentiment analysis the same type of study

researchers conduct when polling an audience.

A computer program, instead of asking people

questions, surveys the Web on a large scale.

Technoloy giants such as IBM and SAS oer

clients software packages that comb the Web forclues to consumer sentiment. Those programs

parse language and attempt to understand the

meaning of words, pairs of words or phrases, in

context.

This has become ever more important as

banks move their customers out of branches and

toward the Web. Those customers are less likely

to walk into a brick-and-mortar building or chat

directly with a teller.

And regulators such as the newly created

Consumer Financial Protection Bureau have

already said they plan on crowdsourcing com-

plaints. That means a bank needs to monitor itspresence on the Web even more closely.

Still, the technoloy is more of an ongoing

study than a science.

Many vendors, and even more computer

scientists and academics, are working to perfect

methods of gauging feelings and emotions across

the Internet without hiring a pollster.

The practice has only recently ltered into the

nancial services industry, and the largest play-

ers in the past several years have just grasped the

benets it could have for business.

In the end, the technoloy could be as valu-

able to banks as face-to-face interactions.

THE STUDY OF SENTIMENTLinguists and programmers have been develop-

ing their brand of sentiment analysis since the

beginning of the last decade.

The science incorporates natural language

processing, which examines sentences and para-

graphs, mimicking the way a researcher might

speak to a member of a focus group. It uses

lters similar to, but more sophisticated than,

programs that spot spam emails.

Its articial intelligence. Not the kind youve

seen in sci- movies. Its more about machines

learning from data and experience sifting

through large quantities of data to inform busi-

ness management and problem-solving.

The simplest method of sentiment analysis

measures good words against bad.

A more complex model has a computer work-ing to gure out phrases and groups of words

associated with positive or negative feelings in

sentences that researchers choose.

If I have 100 tweets, and 50 of them are

positive and 50 of them are negative, what is

the dierence? says Eugene Wu, a database

graduate student in MITs Computer Science and

Articial Intelligence Laboratory. The computer

will distinguish them and look for a word or pair

of tweets that are more positive. [The computer]

can start saying these words are closely associ -

ated with positive feelings.

Methods computer scientists use to gaugesentiment dier according to the geographies

theyre polling, as well as the cultures and busi-

nesses those algorithms are trying to cover.

The technoloy will be someday be able to

tell with certainty whether a person is a buyer or

seller, for instance.

Researchers also try to gure out the subjects

of individual conversations that happen on Twit-

ter or in email messages.

Its called topic modeling.

The question is not if people are tweeting

positively or negatively, but can we di scover

automatically the nature of the underlying topic

that is under discussion, says Philip Resnik, a

professor of linguistics at the University of Mary-

land, who also works at the universitys Institute

for Advanced Computer Studies.

One early application for sentiment analysis

was academics trying to gauge the positions of

movie reviews, says Apoorv Agarwal, a fourth-

year doctoral student at Columbia University.Agarwal last year co-wrote an academic paper on

sentiment analysis of Twitter data.

The reviews, because of their rating systems,

provide a guide for computers to measure against.

Academics later set out to analyze dierent

views taken on a subject in longer online articles.

Further analysis would identify the dierent

moods found within a story.

Related software tools are being used to

target inuencers and determine whose opinion

is given more weight on the web.

Financial services is just one industry inter-

ested in the technoloy. Clandestine federalagencies meaning yes, the CIA and news

organizations are, too.

For instance, The Wall Street Journal uses

sentiment analysis to help its editors create

its Sentiment Tracker feature for its Weekend

Review section, picking out tweets and Facebook

updates about popular topics.

An elementary rst step for many banks is

the basic sentiment analysis embedded in social

media monitoring tools.

Monitoring is just how youve been men-

tioned and how many times. Sentiment

analysis is going to go beyond that, says SethGrimes, a Washington-based consultant who

founded the biannual Sentiment Analysis

Symposium, which attracts business users and

technologists from around the country. (http://

sentimentsymposium.com/)

Some banks are using social media monitor-

ing to nd employees who might be giving away a

little too much information over social networks.

That could potentially reel in a bank executive

unknowingly committing an SEC violation, says

David Wallace, SAS global nancial services mar-

keting manager. SAS oers analytics services that

apply linguistic rules and statistical methods thattease out insight from unstructured text, such as

survey data, email, call center logs and loan appli-

cations as well as social media streams.

BIG-BANK PROJECTSA majority of the b igger banks already employ

sentiment analysis technoloy, says Boxley

Llewellyn, IBMs retail banking director. IBM

provides sentiment analysis services to BBVA

Compass Spanish parent company. Whats new

is we used to look at our enterprise data, now

we look at the enterprise data, the stream data

coverstory

[People] willbe muchmore openand sharing

on theirFacebookaccounts

than if theycalled a

call center.

banktechnews.com August 2012 | BAnk technology news 17

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and the social data, and all the banks are going

to look at all three, Llewellyn says. Thats when

the eyes light up when we talk to banking andbusiness executives.

BBVA Compass is using technoloy from NM

Incite, a joint venture of Nielsen and McKinsey

& Co.

The software picks out posts where the

author expresses positive, negative and neutral

feelings and gives BBVA the ability to react.

BuzzMetrics, the NM Incite tool that BBVA

Compass uses, can show how social media users

comment on the bank.

The software has sped up BBVA Compass

reaction to other trends, as well. Over the past

two months, the bank has started to consider

raising the cash back rewards on its credit cards

because one of its larger rivals was receiving

positive sentiment on its benets.Citigroup has engineered a content platform

called CitiVelocity that boasts a credit sentiment

monitor. It works o of Thomson Reuters Machine

Readable News service, which provides news sto-

ries in computer-readable format.

Citi executives say the tool is purely used for

researching companies on the credit-default

swap index in the U.S. and Europe.

CitiVelocity has been live and free to use for

Citis institutional clients since November.

The monitor looks like a hot-or-not rating.

The most popular companies appear at the top

of one row. The most unpopular fall in another.

A client can click on any company name and

see a chart of how positively or negatively that

company is viewed over a period of time.

A company receives a score of between

minus-1.00 and 1.00 1 being completely posi-

tive, minus-1 being completely negative.

The interesting thing about CitiVelocity is it

brings attention to the names and the companies

that are being discussed, says Ron Papka, Citis

global head of client analytics and market data

distribution. The thing that I nd most incred-

ible is that you can take a stream of language and

a stream of words and it gives you a metric.Meanwhile, American Express has been using

social media monitoring since 2009, prodded by

the nancial tsunami going on at the time, says

Christopher J. Frank, Amexs vice president of its

global market insights group.

You look at consumer condence intervals.

You look at the overall mood. And we wanted

to make sure that we really understood that,

and not just from the American Express point

of view, but what is on the minds of our custom-

ers, says Frank.

Today that technoloy, which is provided by

Visible Technologies, has crept into everythingAmex does. It informs the companys marketing

stratey, and gives it direction on what rewards it

presents to its customers, among other business

practices.

When you look at a series of the programs

that we have released, l ike Link, Like, Love;

Serve and Go Social, we are now taking this

information, and not only looking to take the

pulse, but being proactive, says Frank. Link,

Like, Love is Amexs Facebook app that pres-

ents its users deals and oers based on their

likes, interests and social connections. Serve

is Amexs digital wallet. Amexs Go Social pro-

The question is not if peopleare tweeting positively

or negatively, but can wediscover automatically the

nature of the underlying topic.

Boxley Llewellyn, IBMs retail banking director Frank Eliason, Citis senior vice president o social media David Wallace, SAS global fnancial services marketing

18 Bank Technology news | augusT 2012 banktechnews.com

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coverstory

gram helps merchants create social

and mobile oers and receive detailed

reporting on those oers.

Some large banks consider their

work to be trade secrets. Representa-

tives from TD Bank, Wells Fargo and

Bank of America declined requests for

interviews on this topic. How we track

and analyze sentiment is still evolv-

ing and to some degree a competitive

advantage that we dont necessarily

want to make public, Wells Fargo

spokesman Matt Wadley said by email.

WHERE SENTIMENTANALYSIS FALLS SHORTFor all the work thats been done with

sentiment, some practitioners believe

its too soon to take people out of the

equation.

To get the most out of social listen-

ing tools, I personally believe the best

sentiment analysis requires human

review on every post, says Frank Elia-

son, Citis senior vice president of social

media. Often the numbers are minus-

cule because the tool cannot accurately

determine an authors feelings.

Typically, Eliason says, you see

barely-negative-barely-positive readings

with the remainder of the sentiment of

the message unknown or neutral.

But as you dig into that you willsee numbers drift dramatically when

you use human intelligence, he says.

One company once oered sentiment

analysis of banking, with certain banks

clear winners. As I dug into it, the tool

included topics not even related to the

banks, very much skewing the results.

This has become even more impor-

tant since banks got a big dose of bad

vibes during and after the Occupy

protests in cities across the country.

The movement reportedly led people

to switch from bigger banks to smallercommunity banks and credit unions.

Overall, sentiment analysis technol-

oy is still in early stages.

The devil is in the details, every

specic problem has dierent proper-

ties, so I dont think we are going to

nd a generic solution, says Resnik of

the University of Maryland. But what

is happening right now is we are start -

ing to build metaphorical tool kits that

allow us to get up to our elbows in data,

and gure out what the problems are

and apply answers.

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