Final Project Reflection

For the final project, Bobby and I chose to analyze the transcripts of Presidential and Vice Presidential debates from 1958 to 2012. While viewers may be familiar with the names of Presidential candidates, it can be difficult to keep up with the political stances of all of them, regardless of which political party one associates with most. Though debates can take place over a course of multiple hours, it remains difficult for the average viewer to fully grasp a candidate’s stance on a number of issues. Topics may range from domestic affairs – education and healthcare – to foreign policy matters – terrorism, cyber threats and drugs – and candidates are rarely allotted the necessary time to eloquently express their opinions. Rather than assess a candidates competency on the aforementioned topics, debates serve as a platform to evaluate how they perform under the pressure of the national spotlight. As an alternative to watching long debates or reading lengthy transcripts, data visualization platforms provide the reader the opportunity to quickly expose themselves to topics addressed by individual winning and losing candidates, and examine vocabulary terms utilized by them. After conducting structural and vocabulary analysis of the most recent Presidential debate, Mitt Romney vs. Barack Obama, it was fascinating to learn how the two stressed different ideas in future debates in comparison to previous ones. It was at this point that we decided this was the avenue we wished to continue to explore further.

Prior to continuing, it is important to acknowledge the deficiencies within these data visualization platforms. Visualizations fail to display a candidate’s demeanor when speaking, or how they present themselves to the audience. Illustrating the importance of image, in the 1960 John F. Kennedy vs. Richard Nixon debate, Nixon the ultimate loser stated, “I should have remembered that a picture is worth a thousand words.” Nixon’s comments reveal his regretful attitude for not taking the stage in a presentable manner. In addition to visualization platforms failing to account for facial expressions and body language, they also do not provide the reader context pertaining to the state of the country at the time of the debate. As such, Bobby and I sought to compensate for these faults by providing the reader with analysis across three different visualization platforms, Jigsaw, Gephi, and Voyant, and context concerned with the time period of each debate, collectively providing different analytic perspectives. The majority of our analysis was a result of Voyant of Gephi outputs. The interactive visualizations allowed us to organize the vocabulary used by the winners and losers of each election. The reader would only be hurting himself or herself if they attempted to analyze the visualizations prior to contextualizing the debate. In order to stress the importance of contextualizing debates, all one has to refer to is the most recent Democratic Primary Debate between Hillary Clinton, Bernie Sanders and Martin O’Malley. Prior to the November 14, 2015 debate, terrorist attacks were carried out in Paris, France. As a result, the focus of the debate centered on terrorism, gun control, and number of foreign policy issues in which Hillary Clinton was indisputably the most knowledgeable on. Had the reader attempted to compare this debate to a primary debate in the past or future, the emphasis on these issues would stand out considerably.

On our website, we created four tabs, including an explanation of our iterative research process and the construction of our visualizations, our home visualization page, and a works cited section. We believed it would be most informative if a combination of interactive and static visualizations were created. Within the iterative research process section, it was important to explain how the direction of our research drastically changed from our original plan. The structure of the visualization consists of a timeline that the reader may explore at his or her own pace, with information including notable events that provide context to the debate. In addition, below the descriptions of the debates are links to the Gephi visualizations, permitting the reader to refer back to either the Voyant visualizations, the background information, or view all three options simultaneously. The transcripts have been parsed and separated by candidate, omitting any language that may indicate who is speaking and any comments relayed by the moderator. Afterwards, the transcripts were analyzed in Jigsaw with the intent to discover fascinating trends in relation to sentiment and entity analysis. Next, transcripts were uploaded into Voyant to analyze sentence structure and word frequency. Lastly, the transcripts were inputted into Gephi for the construction of a network visualization of the vocabulary used, separating vocabulary spoken by winning and losing candidates. The combination of Jigsaw, Voyant, and Gephi provided different forms of analysis, which in totality revealed information on not only the issues winning and losing candidates tended to focus on, but the specific vocabulary words they used as well.

One of the biggest dilemmas we faced was deciding if stop words should be implemented in the Voyant visualizations. On the one hand, it can be argued that by utilizing stop words, the debate transcripts have been tampered with. Conversely, it can be argued that applying the stop word feature does remove commons words that do not hold much overall significance. In addition to stop words, as stated on the website, Gephi could not produce useful visualizations when we tried to enter all of the winning or losing candidates data in at once, so we decided to separate them by individual candidates. Also, it  was important to acknowledge that not all visualizations can truly explain the reasons behind a candidates victory. Rather than word choice or attention dedicated to certain topics, some candidates, especially those who lost by a marginal amount, may credit a large part of the reason they lost to their demeanor and actions on stage. When this was the case, it was important to conduct outside research to discover other areas where candidates may have hurt themselves.

The combination of a Humanities and Computer Science student working together on this project ultimately worked out extremely well. Following the completion of this project, I have come to understand that in order to fully maximize the opportunities for discovery that data visualization presents, a team of scholars across different disciplines should be assembled, allowing different perspectives to be voiced. For example, while a Humanities student may not be as technologically adept to particular software, computer shortcuts etc., they are able to contribute a comparative analysis of texts across different disciplines, visualizations, and frame their argument persuasively. On the other hand, the ability for Computer Science students to manipulate and organize information visually in a multitude of way will always serve as a strong asset to any form of text analysis in any research area. Had a political science student joined the team, their expertise may have contributed to taking the project in new directions.

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