Hi, my name is Julie and I’d like to welcome you to the SAP HANA Academy Building Solutions project called Live 5.
Along with the many hundreds of how-to videos we publish for you, we periodically take a simple business use case
and build out a sample solution for you.
You will see how to integrate the various components of the landscape configuration,
the data provisioning, advanced analysis and data modeling,
as well as the application and UI development needed to produce the desired end result.
In these solution projects, we provide the step-by-step instructions through our YouTube videos
as well as provide a repository of code in Github to help you follow along and build the project yourself.
For this project we"ll be showing you how to use SAP HANA express edition
to build out an advanced analysis of Twitter data in real-time
using some of the powerful capabilities of the SAP HANA platform.
For our customers, partners. colleagues and students
it's a great way to easily understand the features of HANA
and be able to get hands-on experience building out a real functioning solution in a matter of hours.
For this project you'll be using the free version of SAP HANA available in the cloud,
which means you can get started right away building your skill sets
and demoing some pretty cool features in no time.
In this video I'd like to take a moment and show you what you'll be building out over the course of this project.
So let's take a look.
First of all, we're building on an HTML5 application using SAP Fiori
and UI5 components so that it can be seen from a mobile device or a browser like I'm using here.
First, I can click the start tracking button here
and then provide a topic like a hashtag that I want to track.
I'll pick FIFA and say OK.
The SAP HANA application is now using the Twitter API to collect tweets that include that hashtag.
If I use this cluster button,
I'm invoking a predictive process to calculate certain attributes about incoming tweets
and to begin to cluster them according to the segmentation of those attributes.
We'll take a look at those attributes in a bit.
Let's refresh our app and ask it to recalculate our clusters.
We can do this a few times since we've just started collecting this information.
You'll see we have more tweets now and our clusters are beginning, are being redefined each time.
In our example, we're looking at a few things
Influence: it's a score that's derived by looking at the popularity of a twitter handle
So, do a lot of people retweet this user?
Is it sent out to a lot of followers? etc
Stamps, which is an indication of the positive or negative aspect of the tweet
and Abuse, which is actually a score based on profanity found within the tweets.
Let's take a look at one of these clusters.
This one is not influential but is highly positive and currently only contains one user
If I click on that cluster, I can drill down and see the individual tweets that were collected and grouped into that cluster.
If I click on another cluster, we can see the negative influence and stance score
indicating a more negative tone to these messages.
You'll also notice that the tweets are in multiple languages.
There are a large set of languages that can be read and interpreted by our text analysis functions.
Here's a cluster that has an abuse score.
If we open that up, we see that our profanity filters change the bad word to all stars.
That's something else we will building into our application.
Let's look at this cluster with more users in it.
Here we see not only different languages but different characters as well.
If we click on the tweet, we could see more information.
In fact, this tweet was a response to a tweet from @CounterMoonbat.
Here we can look at the graph processing results and see that there's a level one relationship between these users
as one responded to another directly.
Another way that we can look at sentiment is through charting our graphics.
In this project we"ll show you how to build out a few charts.
In this first one, we see the collection of tweets grouped by their sentiment rating.
As we saw on the raw data, most of the tweets so far have been positive or strongly positive.
If we click on the strongly negative grouping,
we can filter down and see the tokens or words that gave it that strong negative score.
If we go back and then click on the weak positive score, we see those tokens.
As we collect more data this will become much more useful.
If I uncheck the Sentiment Only box
I can go up a level and take a look at all of the tokens or groups of texts that our analysis created.
For example,
if I click on the URI our URL I could see the list of various links that were contained in the tweets.
Notice that most of these are links to other tweets.
Let's go back and say PERSON.
Here we can see a list of the people identified in the text within the tweets.
If I go back and click on social media topics,
I could see a list of hashtags that were contained in the various tweets we collected on the topic of FIFA.
Notice their hashtags and other languages like Chinese and Japanese that are included in our word cloud.
and last I can go back and click to see the various Twitter IDs or handles used in our data
or I can look at the various topics that are being captured.
So, let's let this run for a few minutes and see if we can see some changes
on the results as we gather more tweets.
Okay, so it's about 15 minutes later and if we refresh our application you see many more tweets, about 1,800 now.
If we click on the Cluster button it will regroup our data into the new clusters
Let's refresh our charting and now see that there's some neutral sentiments that's been added.
You can see it here in green.
If we uncheck the Sentiment Only and drill down into the various text analysis groupings,
we see we have much more information presented now,
more hashtags,
more people mentioned,
more topics.
So that's a quick snapshot of the application that you'll build if you follow this project
In the next video, I"ll walk you through the details of our project
and describe the various components within the architecture
and go over a schematic or data flow diagram.
Please make sure to connect with us on LinkedIn and follow us on social media
to keep up to date with any new additions to this project
or to the many other topics playlists available from the SAP HANA Academy.
Thanks for watching.