UrlAi.com – who are you?


We have created a new service called UrlAi.com, the basic concept is to run blog posts through a bunch of classifiers over time. To begin with we use Gender, Age, Mood and Tonality but the system is dynamic so we can add new classifiers at any time. If you have created a classifier that would fit on urlai.com let us know!

Some ideas

We have many ideas of how we can develop this project further, for example, now we are only showing a summary pie chart, it would be nice to see posts over time. User feedback for online training and classifier improvement may be possible. Another thing we could do is to have classified posts searchable, for example, enabling users to see the mood of everyone who mentioned ‘Avatar’.

Some kudos

Just want to thank the people that has been involved in this project, Roger Karlsson for coding, Johanna Forsman for the awesome logo and Mattias Östmar for sharing his Tonality and Mood classifiers. Mattias has also contributed with many ideas around this, being the idea fountain he is 😀

GenderAnalyzer thoughts

First, thanks to everyone who is testing GenderAnalyzer, we have had incredible feedback. We received emails from many people that are facinated and a few that thinks it sucks =) GenderAnalyzer is still generating a lot of traffic and people are blogging about it.

Our learnings

Determining the gender of an author is not easy, besides the classification there is a chain of technical events that must work in order to get a reliable result. As many of you have noticed the accuracy has dropped to 53% which is far lower than expected based on our tests. There may be several reasons for this low accuracy and I will mention some of them here.

  • Our trainingdata of 2000 blogs is automatically collected from blogspot. Runing internal tests (10 fold cross validation) on this data gives us an accurcy of 75% this effectivly means “Given that the corpus is a perfect representation of real world data, the classifier is able to give any real world data the correct label by a chance of 75%”. So our trainingdata is probably not very representative, as a matter of fact it’s very stereotypical (see for yourself here). Using data from all kind of sources should give us a better model.
  • When someone is testing a blog we are not crawling through posts on the blog to get a good amount of text. We are only hitting the given url and using the text (and html) that appear there as test data. So a page with mostly images or frames will give bad test data. Does anyone know a nice library that – given an url crawls blog posts? Via RSS perhaps?
  • We are trying to encode test data to utf-8 which is the format of the training data – it could be that we are missing some encodings.
  • And of course – the difference between male and female writing is not significant?

What’s next?

We are currently collecting a new set of training data that is much more representative. We will switch to this classifier during the next week and start a new poll for it. It’s going to be very exciting!

Gender Text Analysis

Do males and females express themselves differently in text? Yes is the answer if we look at the research carried out at the University of Texas, in the article “Effects on age and Gender on Blogging” [1] it’s found that author gender can be determined with an accuracy of 80% by looking at a text. This is achieved with a classifier, trained on 37478 blogs written by males and females at blogger.com.

Gender stereotypes in the blogosphere

The research also shows the most discriminating terms for males of females (using information gain).

Male favorite words

– linux
– microsoft
– gaming
– server

– software
– gb
– programming
– google
– data
– graphics
– india
– nations
– democracy

– users
– economic

Female favorite words

– shopping
– mom
– cried
– freaked
– pink

– cute
– gosh
– kisses
– yummy
– mommy
– boyfriend
– skirt
– adorable
– husband
– hubby

They conclude “Male bloggers of all ages write more about politics, technology and money than do their female cohorts. Female bloggers discuss their personal lives – and use more personal writing style – much more than males do.”

Try it on your blog

GenderAnalyzer.com uses the same approach as described in the article, they have collected 2000 blogs from blogger.com written by men and woman. They also have a poll which allows us to see how well it’s working, as we speak it has an accuracy of 70%.

Trying this blog in the analyzer gives us the correct answer

We think http://blog.uclassify.com is written by a man.

[1] J. Schler, Moshe Koppel, S. Argamon and J. Pennebaker (2006), Effects of Age and Gender on Blogging, in Proc. of AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs, March 2006. PDF