- [Instructor] The best thing anybody's ever said
about the videos I make is that they were better than bacon.
Now, I took that as a huge compliment.
And if you're in business
or interacting with people in some way,
then you know it's usually a good idea
to know how things are going over with people.
Elsewhere, I've talked about text analytics,
and you can think of sentiment analysis
as a special kind of text analytics.
You have three major goals.
First, what are people saying about you?
Second, and this is the sentiment part,
how do they feel about what you do?
And then that leads to a natural third one
within your organizational setting is
what can you do about it?
Taking that feedback, the content,
the affective valence of the content,
and then what can you do about it?
Now, the easiest way to do sentiment analysis is
to use a package for a language like Python or R
that has a well-tested dictionary
of affective or emotional terms.
And so you feed the content into it,
and they will actually create a score
for the sentiment value, positive, negative, ambivalent.
And then you can use that as the data
that guides you in your decisions.
Now, there are a few extra complications.
If you want to, you can do something called intent analysis.
This is related.
It says does the message relate to an opinion,
marketing, a suggestion, a query,
a complaint, or something else?
But does it indicate something
that they are planning or intending to do?
And there's also contextual semantic search, or CSS.
And are they talking about the product's quality?
Are they talking about its price?
Are they talking about customer service and so on?
And in each case, what you're doing
is you're taking the sentiment
and you're attaching it to a particular object
to let you know how you need to respond.
Now, there are a few things
that can make sentiment analysis,
like text analytics, pretty tricky.
Number one are differences in vocabulary
by time, by region, by first language,
the use of slang and double entendre.
Any one of these makes the task more difficult,
not impossible, just more difficult.
There are also variations in spelling and grammar.
As I talked about in text analytics,
you need to account for these variations in spelling
and realize that those are fundamentally the same thing
that they're talking about.
Then there's the use of irony and sarcasm
where the written literal meaning is often opposite
of the intended meaning,
and you have to have a way of compensating for that,
especially if irony and sarcasm amount
for a substantial portion of your data.
And then there is the issue of nontext data
in sentiment analysis.
You may go, well, what does that mean?
The most obvious one is emojis as evaluative data.
So each of these emojis which come up in reviews,
come up on social media posts,
they tell you something about how the person feels
about what you've said
or about the product or service you offer.
Now, the nice thing is each
of these emojis has code beneath it
that lets you know what it is, and you can then treat that
largely the same way you treat text,
but then you can start looking at things like memes
that people post.
If it's about your product, your service, your company,
then those become an important part
of evaluating the affective or emotional inclinations
toward you as well.
And then beyond that,
you can actually use sentiment analysis
as a form of added value in other projects.
Just a few days ago, the SentiMATE chess algorithm
which was developed by researchers
at University College in London,
they found a way to do sentiment analysis
to train AI that plays chess.
Specifically, they looked at critics' evaluations
of recorded chess games,
and they're looking for evaluative terms,
things like brilliant or ingenious or foolish or stupid.
Then they use those critiques of chess play
to reinforce the chess playing algorithm.
So that sentiment becomes a guiding principle
in setting up their machine learning
and artificial intelligence
to guide them in the development of their product.
And this is something that can be adapted
in so many different circumstances
where you take that affective label, that sentiment,
and then you use that to guide your analysis
and guide your implementations.
Anything you can do to find more value
and ways to better meet the needs
of the people you're serving is going to be a huge advantage
for your own organization.