Richard and Cynthia Selfe, in the article, Solving Problems in Technical Communication, begins the piece with an anecdote by a technical communications major who had problems trying to explain to others what exactly encompassed the field of technical communications.Through this anecdote, Selfe ultimately poses the question of, “what are the boundaries, artifacts, and identities of technical communication?”.

Selfe describes the various ways that the field of technical communications has been mapped in an attempt to answer this question and better define the field. After discussing the different methods, Selfe explains the disadvantages inherent in these different efforts. The first example was to utilize historical maps of technical communications that outlined the roots of field. This method, Selfe argues, is too limited in that it fragments the field and emphasizes the key moments, while neglecting to contextualize the change over time. The second attempt was to map the boundaries and artifacts associated with research behind technical communications, but these were somewhat limited in scope. The main contributors were limited to academic faculty members and lacked concision. A third approach used was to look at the various skills required in the field. This was seen as advantageous in that it was dynamic and future oriented, but lacked in its ability to cover the subjects and fields where technical communications is utilized.

Selfe continues by introducing the use of word clouds to map technical communications, which is essentially a list of words that vary in size to denote its frequency. This is argued to be a better alternative to past methods as it allows people to look at a larger scope of the field of technical communications by presenting a wide breadth of textual data that is combined with visual information showing frequency. Using this information, one can quickly interpret the data and figure out the importance that various tags have to this field. She then continues by utilizing these techniques in an effort to map the field of technical communications.

I applied Selfe’s topic and mapped the words in Obama’s latest State of the Union address. Using this method, we are able to see what kinds of focus the speech had and understand what it is that the White House saw as important issues this year.

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Looking at this word cloud, we can see that the word jobs and work stand out among the most frequently mentioned word. Job creation has been a hot topic because we were at the tail end of a recession that resulted in high rates of unemployment. Moreover, jobs were mentioned in relation to the stimulus package passed in 2009, which the result of have manifested over the last few years. Because of this, words like invest, manufacturing, develop and economy show up at relatively high frequency. Education is also mentioned quite often and as a result words like college, children, schools and students are have a high frequency of use. It’s clear, however, that most popular topics are also not highly contentious issues.

A by-product of charting frequency is the ability to identify topics that have a significant infrequency. In this example, one can determine which topics the president shied away from after looking at the word cloud. Controversial topics like gay marriage, abortion and the war on drugs are noticeably absent in this speech. Although specific conclusions cannot be derived from these observation, looking at which topics were omitted speaks to the political and social climate of the time.

Overall, utilization of the word cloud left much to be desired. Much context is lost when single words are isolated, and although there are ways this can be mitigated, there is a likelihood that analysis of verbose documents will result in loss of crucial details. Furthermore, it’s difficult to track change over time in a single word cloud. Utilizing multiple word clouds that compare similar documents over time, however, may allow more dynamic analysis of topics.Although there are a number of crucial limitations that become evident upon implementation, word clouds are useful tools in that they allow a wide range of textual data to be sorted through quickly.

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