Linguistic Analysis of the State of the Union Addresses

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This weekend I harvested 231 State of the Union addresses up to 2017 and put them through NLP processing.

Here are the unigram TF-IDF values, generated with this code of mine (full output: sotu-1-gram).  Each file is named with "YYYYMMDD-Name" format.

1. 17900108-Washington.txt
  1. licentiousness = 0.0045366833
  2. intimating = 0.0045366833
2. 17901208-Washington.txt
  1. misemployed = 0.0036363261
  2. empowers = 0.0036363261
  3. residuary = 0.0036363261
...
230. 20160112-Obama.txt
  1. isil = 0.0058471495
  2. retrain = 0.0016751325
  3. rigged = 0.0013369885
  4. brothers = 0.0012484422
  5. online = 0.0012484422
  6. automated = 0.0008375663
  7. cop = 0.0008375663
  8. peddling = 0.0008375663
  9. clocked = 0.0008375663
  10. spilling = 0.0008375663
231. 20170228-Trump.txt
  1. obamacare = 0.0043560855
  2. oliver = 0.0034848684
  3. megan = 0.0034848684
  4. susan = 0.0034848684
  5. megan's = 0.0034848684
  6. jenna = 0.0034848684
  7. jamiel = 0.0034848684
  8. jessica = 0.0026136513
  9. denisha = 0.0026136513
  10. shaw = 0.0017424342

And here the bigrams (full output - sotu-2-gram):

1. 17900108-Washington.txt
  1. work allowed = 0.0161891231
  2. inviolable respect = 0.0161891231
  3. fund designated = 0.0161891231
  4. punish aggressors = 0.0161891231
  5. learning already = 0.0161891231
  6. though arduous = 0.0161891231
  7. also render = 0.0161891231
  8. government receive = 0.0161891231
  9. competent fund = 0.0161891231
  10. particularly recommended = 0.0161891231
2. 17901208-Washington.txt
  1. case submitted = 0.0132045362
  2. national impressions = 0.0132045362
  3. northwest side = 0.0132045362
  4. shall cause = 0.0132045362
  5. attention seems = 0.0132045362
  6. uniform process = 0.0132045362
  7. peculiarly shocking = 0.0132045362
  8. stock abroad = 0.0132045362
  9. friendly indulgence = 0.0132045362
  10. us abundant = 0.0132045362
...
230. 20160112-Obama.txt
  1. pass muster = 0.0050289617
  2. big question = 0.0050289617
  3. unarmed truth = 0.0050289617
  4. unconditional love = 0.0050289617
  5. respects us = 0.0050289617
  6. many issues = 0.0043884723
  7. economy contracts = 0.0025144808
  8. everybody willing = 0.0025144808
  9. new terrorist = 0.0025144808
  10. offering every = 0.0025144808
231. 20170228-Trump.txt
  1. joining us = 0.0098689435
  2. american child = 0.0064590250
  3. th year = 0.0049649083
  4. rare disease = 0.0049344718
  5. incredible young = 0.0049344718
  6. megan's life = 0.0049344718
  7. jamiel shaw = 0.0049344718
  8. recent threats = 0.0049344718
  9. jessica davis = 0.0049344718
  10. republican president = 0.0049344718

The lexical diversity is shown in the following graph:

R> df$file[ which( df$div == max(df$div) ) ]
[1] "17901208-Washington.txt"

R> df$file[ which( df$div == min(df$div) ) ]
[1] "19460121-Truman.txt"

The reading level has steadily declined, as shown in this graph:

R> sotu$Name[ which( sotu$FOG == max(sotu$FOG) ) ]
[1] 18151205-Madison

R> sotu$Name[ which( sotu$FOG == min(sotu$FOG) ) ]
[1] 19920128-Bush

(And here are two excellent sites with their own analysis: http://www.presidency.ucsb.edu/sou.php & http://stateoftheunion.onetwothree.net/)