In the early hours of November 7th, 2012, Barack Obama took the stage at the McCormick Center in Chicago to give his re-election victory speech after Republican challenger Mitt Romney had conceded the defeat of Ohio, and as Ohio went, so went the nation.  In about 20 minutes, Obama thanked his family, his friends, his supporters, and laid out a vision of what American is, can be, should be, and will be.

Now, I am not posting about his speech because I am a liberal.  No, I am posting about his speech because I like to play with data and being the ever curious scholar, I wanted to see what Obama’s victory speech would look like as a tag cloud.

The use of word clouds or tag clouds is growing as a means by which to illustrate qualitative data analysis and content analysis.  Analysts are using this technique as a means to reduce big data into descriptive accounts of how frequently certain words or ideas or categories manifest in relation to the context in which it appears.  Klaus Krippendorff, in his latest book on content analysis, has endorsed the method as a means for displaying content analysis, and other researchers (such as McNaught & Lam) have utilized it to gain control over and make sense of qualitative data.

I utilized it in an early analysis for my Virtual Worlds Entertainment project when I sought to understand the questions novices had when engaging with Second Life and City of Heroes.  I broke their questions down into three parts: what type of question they asked (based on the types of questions journalists ask), what aspect of the virtual world they were asking about (themselves, the interface, their avatar, etc), and what about that aspect was causing their question (the world’s purpose, the avatar’s capability, their reasons for playing, etc).  I analyzed their questions for themes that emerged for these parts, and then I used these themes to code their questions, resulting in finding the frequencies for how often the themes were mentioned by the novices.

I then used as a free, online tag cloud generator to display how frequently each theme was mentioned by the novices.  The more frequently the theme was mentioned, then the larger it appeared in relation to the other themes for that analyzed part of the questions.  I did two tag clouds for each part of the questions: one for Second Life and one for City of Heroes.  Doing so would make it possible to compare the questions for the two different virtual worlds to see if different themes were more frequent in the one versus the other.  For example, here are the tag clouds for the analysis of what aspect of the virtual worlds the novices had questions about.

What they were questioning in City of Heroes
What they were questioning in Second Life

What I like about this technique of using tag clouds is how visually striking the frequencies of themes are, and how the qualitative nature of the data is retained in the visual portrayal.  Now, to be fair, these examples from my study are not retaining the discourse as given by the novices in their interviews: the words displayed in the tag clouds are my interpretations of what they said.

But the same striking visuals can be developed by using an entire transcript and retaining exactly what the person said as it was said.  What is analyzed then is not the frequency of a theme or a code, but the frequency of an uttered word.  The interpretation of such a tag cloud could be that the words used most frequently by the person have some type of rhetorical, discursive or sense-making meaning to the person.  If a person says “I” more than “we” when discussing a relationship with a significant other, then this could be interpreted to indicate a narcissistic perspective on the relationship.  If “I” and “worry” are used a lot in this situation, it could be interpreted as insecurity about the relationship.

So, on a lark, as I was discussing how to utilize tag clouds for visualizing data to my research methods undergraduate class, I decided to utilize an online transcript of Obama’s entire victory speech, which had any indication of audience response removed, such as “applause” and “cheers”.  By inputting the entire transcript into Wordle, what resulted is this tag cloud:

  Now, what I find striking about this tag cloud is not the extent to which “country” and “America” are at the forefront in frequency: one would expected a newly elected president to mention these descriptors of the people who had elected him. What I find most interested is the extent to which his attempt to use inclusive rhetoric in order to develop this discourse of unity.  Look how large “every” and “together” are — far larger than than “American” or “family” or even “hope”.  Yes, the President’s campaign message of moving forward, focusing on the future, are also dominant, but from this tag cloud one also gets the sense of the importance of his message that America is best as a nation when it works together, when everyone is helped upwards and onwards.

This type of inclusive rhetoric makes sense given the need he has to build a coalition in a very fractured country.  At the end of the speech, he returned to one of his old messages from 2004, regarding how we are not blue or red states but the United States.  That line was a take away quote for the night, but that rhetoric of unity, according to this tag cloud, was prevalent throughout the entire speech as one of the speech’s main themes.

Now what would be interesting is to compare tag clouds for this speech to the comments being made by those who wish to secede from the country given his re-election victory.  What would Wordle show as some of the most frequently used terms in their rhetoric?

2 responses to “Analyzing Obama’s 2012 Victory Speech”

  1. Monette Bebow-Reinhard Avatar

    That was an awesome article! Thanks for sharing this fascinating research.


  2. Analyzing Obama’s 2013 SOTUS « Playing, With Research Avatar

    […] after the election was determined, I used the tag cloud generator to analyze Barack Obama’s acceptance speech.  Just to follow-up, I decided that the same type of analysis would be usefully applied to […]


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