Dexter and Python initially started as a Geocities website when I was in 6th grade. Over the years, it has slowly evolved into a place where I can practice and become familiar with web development as it progresses. I started out coding with Microsoft Frontpage, then moved to Dreamweaver. Initially, there was very little coding, and it was all WYSIWYG. However, as I learned more, I could make it more interactive.

I incorporate drawings and blog posts to keep those that are interested updated. Through the evolution of the website, I was forced to learn HTML, PHP, CSS, XML, jQuery, javascript, and the handling of MySQL Databases. I've put online some code samples.
Dexter and Python

Between the many episodes of Dexter there comes a time when an update for this is long overdue. Last blog post I mentioned the rally that I would be attending. It was a 15 hour bus ride there and back, and it was chaos from the moment we left to the moment we got back, but overall, I had a great time.

Now that the semester is over, I have some time at home. I was planning on scanning the various doodles notes that I had taken during my classes. Unfortunately, I left my notebooks back at school, so this will have to wait for a bit.

Last semester, I took a Computational Linguistics course, which required me to get familiar with Ubuntu, Linux, and Python 3.0. I made various programs: calculating a chi square(*.py code here), learning regular expressions, and a multitude of other assignments. The final project was free-form, and I was able to choose whatever subject (within reason) I could. The ultimate goal was to demonstrate my knowledge of both regular expressions and Python 3.0.

I considered a lot of topics, but ultimately ended up with a project on "Analyzing Swear Frequency by Gender" Why? Well, it was more interesting than just analyzing something like "medial consonants." (See! Nobody know what that means.) Plus, once I got my data, I could use it to stereotype.

I ended up going through the spoken part of the American National Corpus and extracting various information about who (Gender, Age) was speaking at a time that a swear was uttered. I compiled all of this information, and obtained the following:

2400 Conversations Examined (Each conversations has two speakers)
2412 Males spoke
2380 Females Spoke
8 Undetermined Gender
Males Swore 138 times.
Females Swore 38 times.

You can see all of this, as well as more data on the ages (and specific swear frequencies), at the bottom of the output_file. As you can see, the age range was slightly higher than I would have hoped(despite the fact that I was being conservative in my age).

You can see a breakdown of the entire project in the write-up(*.pdf, 109KB) that was created.

To download the actual python file, click here.(*.py, 10.7KB)

Overall, it did tend to show that males swore much more than females do. But there is great possibility of bias (discussed in the write up). For one, the people were speaking with strangers, and thus wanted to make a good impression. Thus, they wouldn't swear as much as they would if they were conversing with friends (which is what I wanted to examine). I also had hoped that my overall swear count would be bigger, but the only way this could happen would be to use a bigger corpus.

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