[Music]
hello
and welcome to solving with aws
solutions your deep dive in
architectures
built by aws that are ready to deploy
instantly in the cloud
i'm your host sean and i'm joined here
with fellow builder and qriket legend
nihit
he's going to be walking us through an
aiml-based solution
that helps customers better understand
the hot topics surrounding their brand
our customers tell us that understanding
public perception about the brand
is critical for managing their business
they would like to get
real-time insights and so that they can
respond quickly
there's a lot of different twitter
sentiment engines out there nia tell us
why this one is different
this solution goes beyond simply
aggregating keywords or key phrases
or doing a basic sentiment analysis it
uses machine learning
to identify the hottest topics being
discussed and help
customers understand the context behind
well let's jump right in
all right let's take a closer look tell
me about the solution
so this is the landing page of the
solution on this page
is a link to the source code the
cloudformation template
uh deployment guide as well as a
button to launch directly into your web
console
oh so you can get up and running pretty
quickly absolutely okay
well let's dive into the architecture
how did you design this
so the architecture overall can be
visualized as three main
components right the leftmost side is
the ingestion portion of it
in the middle is the workflow and the
orchestration of the various ei services
right and on the right side is storage
and visualization
of the inference events that you would
receive from the machine learning
inferences
okay that seems pretty reasonable well
let's let's look in the ingestion phase
it looks like it gets kicked off with a
simple api call
yeah so there is a lambda function right
the lambda function is triggered through
a cloudwatch event rule right and it
calls a twitter search api
to pull in feeds and these feeds are
then
pushed through a kinesis data stream on
the other side of the kinesis data
stream is a lambda consumer
that triggers a step function workflow
to orchestrate the
various ai services i see and it looks
like you're using comprehend
translate and recognition that's right
sean so
the solution allows you to create a
filter
on the tweets that you want to ingest as
a the filter defines a list of
languages for the fee for the tweets
that you want to pull in
right and translate does the job of
converting
non-english tweets to english before
subjecting it to comprehend for topic
modeling sentiment analysis
entity and key phrase detection i see so
everything gets translated to a baseline
language of english
and then you put it through the pipeline
for now i see
and recognition then does the job of
detecting
embedded text within the images as well
as any unsafe content
something which is violent any negative
imagery that may be associated with the
brand
i see i see so that will alert customers
in case there is something that might
you know trigger something worth digging
into right so you mentioned detected
text
and the moderation labels how do those
get
output into the analysis so the detected
text goes through the same
comprehend processing uh topic modeling
entity and key phrase detection
sentiment analysis okay
and the moderation labels are available
as inference events which can be
visualized
from a brand context perspective okay
and i see here you're using eventbridge
why did you take an event-based
architecture here
so for this solution there's only one
target that
we have defined for the event bridge
basically to store all the events
to an s3 bucket but the even bridge
provides the flexibility for customers
to extend the solution to add additional
targets
and define additional rules for based on
their use case
i see so if they want to have multiple
consumers of the data
this is where they plug in that's right
okay perfect
um well do we get to look at the
dashboard sure
okay so this is a topic analysis
dashboard oh wow
on this dashboard we have all the
dominant topics
that the solutions detected i see here
so you got zero zero zero through zero
zero
nine i think that's right uh so
the solution is configured to detect ten
topics but
through a cloud formation parameter you
can detect up to 100 dominant topics
okay so these are like the the biggest
topics that people are talking about
with respect to my brand that's right
and zero zero zero being the most
dominant one
i so the second row of visuals then
focuses
on zero zero zero which is the most
dominant one and
brings in all the phrases associated
with that dominant topic as well as the
sentiments and the corresponding tweets
i see so the first row is all ten topics
then we drill into the most dominant one
and i can see the sentiment analysis of
it the tweets themselves
and the word cloud of the most dominant
terms in that topic
right that's great got it the third row
is a word cloud of all the freezes
within the
within those dominant topics and based
on
whatever the user is interested in you
can click on a specific one so in this
case i'm clicking machine learning
okay and that refreshes the content for
the other two visuals
okay so you can dive a little bit deeper
into a particular term within a topic
that's right
okay great so that makes sense from a
comprehend perspective
but what about from the recognition uh
what comes where's the output of that
analysis
so if i switch to the image analysis tab
here is where
the inferences from recognition are
visualized
the first row of visuals shows whatever
embedded text
that recognition detected for us and the
second row of visuals
is the moderation labels and any unsafe
imagery
that it found associated with the tweets
that it ingested
i see so you're really looking at two
things here text within an image
and then the image itself and if there
are any flags that
recognition detected with respect to
violence or suggestive
content right and selecting any one of
them
actually refreshes the content to help
customers dive deeper
into understanding the context behind it
i see how that could be pretty helpful
i really like how you designed it it's
serverless it's easy to get started with
and there's extension points
thanks for walking me through my
pleasure thank you
[Music]
this is a great way for you to go beyond
sentiment analysis and understand the
hottest topics that are impacting your
brand
you can use this solution to augment
with your business strategy
around product launches service
announcements crisis management
public relations and in general to
understand public perception
go check out discovering hot topics with
machine learning and other solutions on
the aws solutions website
see you next time