Learn how to do Twitter sentiment analysis without having to write a single line of code
— inside the video!
Hey, guys, it is Chia from Brand24 and this week’s video is about sentiment analysis
on Twitter, so
I’m going to explain what sentiment analysis is; how to see sentiment on Twitter
and then I’ll show you how to do Twitter sentiment analysis without having to write
a single line of code (you do not need to know any programming languages like Python
or R for this).
What is sentiment analysis?
Well, sentiment analysis is defined as the process of using natural language processing,
text analysis and computational linguistics to identify and then categorize the opinions expressed
in a piece of text — especially in order to determine the polarity of the
opinions, which can be positive, negative or neutral.
So, in short, sentiment analysis examines human beings and our subjective opinions or feelings
on a particular topic.
And when we apply that to business or marketing, it helps us understand how these opinions
and feelings can drive consumer behavior.
Human beings are not exactly known for being rational creatures.
In fact, in 2017, economist Richard Thaler — he was awarded the Nobel Prize in Economics for:
“his contributions to behavioral economics and his pioneering work in establishing that
people are predictably irrational in ways that defy economic theory”.
We humans are a very complicated species, and it’s hard to predict how we’ll behave.
I mean, how is there so much controversy right now over wearing a mask during a pandemic?
And what makes my cousin support one presidential candidate while I support the other?
Why the heck are Crocs so trendy?
So, you see, people are kinda weird (and that’s what makes us so interesting) but this is
why there are so many books and fields of study dedicated to understanding the driving
forces behind human behavior and relationships, such as: psychology, sociology and behavioral
economics.
Our individual perspectives ensure that every human experience is as subjective as it is
relevant, and
this is where sentiment analysis comes in: the beauty of analyzing sentiment is that
it can help us identify and then make sense of all the differences and similarities that
we share — and platforms like Twitter, which is overflowing with sentiment-filled tweets,
make this data more accessible than ever.
We can see in these live Twitter statistics that nearly 500 million tweets are sent every
day, that's approximately
6,000 tweets per second… each of them expressing opinions and feelings
on every topic imaginable.
And it only takes seconds to analyze the data from millions of tweets with advanced sentiment
analysis tools.
We’ve compiled a current list of the top 17 sentiment analysis tools in a blog post
(with a brief review of each tool) and I’m throwing a link to it in the Description Box
for this video, so you can check that out below to find some good sentiment analysis
tools... before I show you
how to automate Twitter sentiment analysis (without having to write
any code, like I promised) with a sentiment analysis tool, I’ll quickly go over how
to view twitter sentiment manually on Twitter, and you can do this using Twitter Advanced
Search commands.
It’s really easy: just go to twitter.com and in the search bar, enter your hashtag
(or your keyword or phrase, depending on your topic of interest) followed by a smiling emoticon
to see tweets with positive sentiment or a frowning emoticon for tweets with negative sentiment
Let’s look at tweets with positive sentiment for the hashtag #crocs… that’s kind of cute...
it’s interesting to look through, but as you can see, Twitter doesn’t show
you the total number of tweets for this hashtag (I explain how to see the number of tweets
for a hashtag in this video, which I’ll also link to in the Video Description box
below) so needless to say
Twitter also doesn’t reveal the number of tweets with positive sentiment
or the number of tweets with negative sentiment… so the information available here won’t
be quite enough for researchers, data scientists
or any business professionals who need to
conduct a real Twitter sentiment analysis.
To take sentiment analysis on Twitter to the next level, we can see what it looks like
when the process is automated with an AI-driven sentiment analysis tool like Brand24, which
is powered by machine learning and natural language processing.
Here, we can access the total number of tweets about Crocs, as well as the number of tweets
with just positive sentiment or just negative sentiment... we can see when they were tweeted,
by which accounts, and more...
I’m going to add another link in the Video Description box — to a blog post that shows
you step-by-step how to do Twitter sentiment analysis, so you can find that below as well.
Apparently, people really like their rubber clogs… and for a bunch of different reasons.
Now, if you don’t want to use a sentiment analysis tool because you prefer to do this
manually or because you enjoy writing code and programming, you could also analyze Twitter
data with Python or R. This would require first applying for access to the Twitter API,
installing your dependencies and then writing your own sentiment analyzer script.
There are a lot of great resources right here on YouTube that show you how to do this, though
they do require some basic knowledge of programming.
Luckily, for those of us with very limited coding skills, we can still analyze sentiment
on Twitter with the help of some good sentiment analysis tools.
Alright guys, that’s it for this week!
Thank you for watching, I hope this was helpful, and I’ll see you next time, bye!
(humming)
guys, it is Chia ... oh, wait... (laughter)
ah, coffee time...
I still have the last few drops of this
mmm... (slurping noises)
everything tastes better with a straw