Tinder Users More Likely to Tweet About Positive Things Than Negative

If I had to guess, I’d think people who were posting about Tinder would have a lot more negative things to say than positive.  Creepy messages, no-shows for dates, fake profiles… there are plenty of negative things out there.

But after digging into the data, I was surprised to find that, on average, the tweets about Tinder contain more positive words than negative:


I tagged words used in the tweets as either negative or positive, and increased the negative or positive score if the words had things like “very” in front of them.  The most negative words had a score of -1, and the most positive had a score of 1.

As you can see, most of the words fell into the slightly positive category, between 0.0 and 0.2.

I did the same thing for OkCupid and eHarmony tweets.  They had a generally similar trend, but were slightly more on the positive side than the Tinder tweets.

I wanted to know more.  What were the most common negative words in the tweets about Tinder, OkCupid, and eHarmony?  To find out, I created a word cloud.  The more common the word, the bigger it appears.


(A brief note about why some of the words look funny:  I used a technique called “stemming,” which groups similar words together by chopping off the end.  For example, “desper” includes desperate, desperation, etc.)

People are tweeting about some scary stuff!  Attack, scary, devil, panic, death.

Negative words like:  hate, weird, stupid, desperate.

Other things include:  garbage, fraud, freak, and drunk.

(By the way, the tagging set I used classifies the word ‘fun’ as positive and negative, presumably to include when people use it in the context of “making fun” of someone.)

What about the positive side?


Positive words include “funni” (for ‘funny’, ‘funnily’ etc), and of course, love.

Friend, success, amazing, hot, strong, caring, genuine–we can see what people are hoping to find when they tweet about online dating.

On a technical note:  I used the open-source tool KNIME to collect the tweets and do the analysis.  For more on how I did it, check out my blog post on the KNIME website.