The analysis used the method for text network analysis. The basic premise of this approach is that every word is represented as a node and their co-occurrence within the same context is represented as an edge in the network. After a series of transformations (performed by Textexture software developed by Nodus Labs) the graph is produced, which is then aligned according to Force Atlas algorithm. The nodes (words) that are connected (co-occur within the same context) are pulled together, while the nodes that are not connected are pushed. The resulting aligned graph gives a very good representation of the major semantic fields present within the text. Furthermore, community detection algorithms are applied to the resulting network, sorting the nodes (words) into the different groups according to how interconnected they are to one another. Every community is represented with a different color. As a result, if two words co-occur often together inside the same text they will be positioned next to each other on the graph and also belong to the same community (and, thus, have the same color on the graph). These communities represent the topics inside the text. Finally, the nodes are ranked according to their betweenness centrality measure: the bigger the node, the more different communities it belongs to.
It’s worth noting that such approach is very different from so-called “tag clouds”. Tag clouds show the most frequently mentioned words and they rarely position these words according to their proximity within the text. Therefore, one can get a general idea of the vocabulary inside the text, but it’s very hard to have a sense of the meaning that is produced using this vocabulary. Text network visualization, on the other hand, emphasizes both the most frequently mentioned words, as well as the relationships between them, making it much easier to understand what the text is about. Furthermore, it can also detect the topics inside the text, making it a much more useful tool for improving text comprehension and providing a much more useable interface for text navigation.
Bush, 2001:
Bush, 2005:
Quite a generic agenda at first sight, however,
Bush was the first one to introduce the notion of “time” and use it to motivate
certain policies. It’s all about the Now: “In all of these ways, I will bring
the values of our history to the care of our times.” Not surprising that the
“story” is also such an important concept in his speech: it’s full of short
stories. In 2005, after the re-election is over, Bush is running the second
term, probably thanks to his emphasis on “freedom” and “liberty” – a trick that
always worked in the US and that was successfully employed by Reagan in his
second term (see above).
Obama, 2009:
Obama, 2013:
The master of rhetorics, Obama combines the best of his predecessors in this inauguration speech. No wonder the “word” has such high relevance in his speech – it refers to the moments Obama is quoting someone else. In 2013’s speech the “time” and “require” probably relates to the fact that Obama had to respond to all the criticism that something had to be done immediately about the state of US economy and politics – and he successfully addressed these concerns.
Source:
See more, about the text network analysis:
http://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis/
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