In this episode, we talk about feature engineering and text, especially social media content. Social media text is often accompanied by very little data, so what can we do to enhance and expand it? That’s feature engineering. Watch the video for details about what kinds of features we can add to social media content to make it more understandable.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode of Fun fact, Friday this week at the trust insights annual summit, which
was held in my dining room, because we’re startup
we were talking about some of the different ways you can do
data tasks like feature engineering because the ability to use topic modeling and text mining and all these different machine learning algorithms is contingent upon what you put into the algorithm.
The more data that you have that’s valid, clean, compatible, chosen, well, the better the algorithm will perform.
When you think about it from that lens, then
the average piece of texts that you feed to
an algorithm, it isn’t very rich,
for example, take a tweet, right? 280 characters at most probably 1015 words
that in and of itself, this not a lot in that it’s just a bunch of words, what are the things that you can extract from that that could help you to better understand it better quantify it and and build better predictive outcomes around
feature engineering is a data science term, which means, well, it’s engineering features. A feature is a dimension if you think about
putting a tweet in the first column of a spreadsheet, right? And you put all your tweets in one column that is one feature the text itself, what other columns could you add to that spreadsheet that would be meaningful, that would describe the data that you could engineer out of that single tweet you can get from Twitter, for example, the author, you can get the date and the time, those are useful meta attributes that could provide some clarity about
the tweet itself, you know, tweets sent in the middle of the night might have a different point of view, different language than a tweet sent in the middle of the day,
if you run automated sentiment analysis, you can start to pull out things like specific emotions that are tagged from
very well known libraries like UD pipe,
you can get this the sentiment numbers plus one minus one and the scale along that line. And you can start you can extract things like character count, word count, number of capital letters, number of emoji in in
in a tweet, for example,
why would you want to do this? Why would you want to add more columns, given that we’re already talking in many cases about all this too much data, and there’s too much they have for us
to analyze? Well, the reason you want to do feature engineering is
you want to be able to start chipping away at the why now, no amount of this very clearly no amount of feature engineering can substitute for good qualitative research, no amount,
but it can help enhance your qualitative research.
It can give you more interesting questions to ask in your qualitative research. And it can eliminate things
that are might be questions you would ask and qualitative research if you can show that there’s no relationship whatsoever.
So for example, time of day, if you do feature engineering, and you have the data and you run it through
an algorithm that says, hey, time of day, it’s not important to the end result that you care about,
then you can eliminate questions in your in your focus group about time of day if there’s
complete lack of statistical significance about time of
day because say, okay, we don’t need to ask that question. It can make your
your qualitative research more focused. The other thing that
feature engineering does is it helps you
start to, to understand
hidden relationships within your data
that you might not think to ask. Otherwise, if you use a
user, build your own feature engineering library,
you can have it do very specific things like how many capital letters do something using that’s, that’s not something that a person who’s analyzing, say, a book might think about, because it’s not helpful. But think about the people in, for example, social media conversations, if you got a tweet, that’s all in caps, either the caps lock key was stuck on a keyboard, well, that person’s really angry and wants to yell really, really loud, well, then us angry, they want to yell really, really loud.
That’s a feature that knowing how many capital letters, or what percentage of a social media post is all capital letters is
actually could be a distinguishing feature, especially when you pair it with something like emotion.
And if you have a target variable, like number of clicks, or
number of retweets, or shares or comments, or whatever it is, you want to use your endgame
metric, then creating all these additional features could help you understand what are the little subtleties within that text that
indicate the up there’s a there there, there’s something else to dig into this deeper to dig into. So
give some thought, when you’re doing your social media analytics, when you’re doing your marketing analytics, when you’re doing
your blog analysis, your content marketing, give some thought to how you analyze text, how you extract features, what features you look at, remember, features or columns in the spreadsheet,
and ask whether you’ve done enough
on the feature engineering front to find hidden meaning. Now, a a prerequisite of feature engineering is that you’ve got to have an algorithm that allows you to analyze all these features in combination and figure out which ones that matter the most
trusted insights this we use three or four different algorithms depending on the type of data set and what’s in it.
But all of that software is free. It’s open source software, academia has done an incredible job, the academic world of sharing working code for all of us to do enjoy and use important to make sure that academia remains well funded for
that very reason.
But the answers are knowable. And I think that’s probably the most important thing of this entire Fun Fact Friday is that
the code is available, the data is available, we have to be the ones to engineer the features
unless you use deep learning, in which case it can do automated feature engineering.
it’s just a matter of putting the pieces in the right order and having everything prepared well, to do this extraction, what do you get out of it, at the end,
you get a model that says these are the top five or 10 things that create
should not create that have a high statistical correlation to the end metric you care about. So if it’s retweets, for example, these are the five or 10 things that matter most out of this library of
for example, I am working right now with a data set
that is medium posts 1.4 million medium posts and
the this is a massive massive data set
and there’s an end target
collapse and then there’s not much else right so I have to engineer
about 15 new variables in order to do the feature engine but at the end of the day I want to know what has a high mathematical relationship to collapse and then we use that as the basis for
our testing plan to say okay let’s go and test to see if we do more of X y&z do we get more claps
at the end on medium
that’s what you do with this that’s the value of this is gives you a much more focused testing plan. and that in turn means that your marketing can get more effective. So
feature engineering is the name of what you do in data science to get
at this stuff, try it out
dig into your data you already have and see what’s in there see if it if there’s value that is hidden within your data and see if you can use feature engineering and then some machine learning statistical techniques to
to unlock that
value. As always, please please please leave comments and questions in the comments box below and subscribe to our YouTube channel and I newsletter and I’ll talk too soon.
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