Word Analysis of 2016 U.S. Presidential Debates

Tim Kaine vs Mike Pence

4 October 2016

Word Statistics

Debate Word Count

Summary Word Count

The summary word count reports the total number of words and the number of unique, non-stop words used by each candidate. Word number is expressed as both absolute and relative values.

Table 1a

all words

Number of all words and unique words used by each speaker.

set

word count

Tim Kaine

7,502 download text •	1,423
48.8%	19.0%

Mike Pence

7,884 download text •	1,436
51.2%	18.2%

total

15,386	2,175
100.0%	14.1%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 1b

exclusive and shared words

Words exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).

set

word count

Tim Kaine

1,149 download text •	739
15.3%	64.3%

Mike Pence

1,251 download text •	752
15.9%	60.1%

both candidates

12,986 download text •	684
84.4%	5.3%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 1

legend

a	c
b	d

a :: word count

b :: word count, as fraction in total in debate

c :: unique words in (a)

d :: unique words in (a), as fraction in (a) bar :: proportion of (a-c):c

Table 1

commentary

Stop Word Contribution

In the table below, the candidates' delivery is partitioned into stop and non-stop words. Stop words (full list) are frequently-used bridging words (e.g. pronouns and conjunctions) whose meaning depends entirely on context. The fraction of words that are stop words is one measure of the complexity of speech.

Table 2a

non-stop words

Counts of stop and non-stop words.

speaker

all

stop

non-stop

Tim Kaine

7,502 download text •	1,423
100.0%	19.0%

3,984 download text •	149
53.1%	3.7%

3,518 download text •	1,274
46.9%	36.2%

Mike Pence

7,884 download text •	1,436
100.0%	18.2%

4,152 download text •	136
52.7%	3.3%

3,732 download text •	1,300
47.3%	34.8%

total

15,386 download text •	2,175
100.0%	14.1%

8,136 download text •	154
52.9%	1.9%

7,250 download text •	2,021
47.1%	27.9%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 2b

exclusive and shared non-stop words

Non-stop words exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).

set

word count

Tim Kaine

1,112 download text •	721
31.6%	64.8%

Mike Pence

1,237 download text •	747
33.1%	60.4%

both candidates

4,901 download text •	553
67.6%	11.3%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 2

legend

a	c
b	d

a :: total number of words, for a given category (all, stop, non-stop)

b :: (a) relative to words in the debate if category=all, otherwise relative to words by the candidate

c :: number of unique words with set (a)

d :: (c) relative to (a)

bar :: proportion of (a-c):c

Table 2

commentary

Word frequency

The word frequency table summarizes the frequency with which words were used. I show the average word frequency and the weighted cumulative frequencies at 50 and 90 percentile. The average word frequency indicates how many times, on average, a word is used. For a given fraction of the entire delivery, the weighted cumulative frequency indicates the largest word frequency within this fraction (details about weighted cumulative distribution).

Table 3a

word use frequency

Average and 50%/90% percentile word frequencies.

speaker

word frequency

all

stop

non-stop

Tim Kaine

5.3 download text •	19	239

26.7 download text •	80	283

2.8 download text •	5	26

Mike Pence

5.5 download text •	23	260

30.5 download text •	93	431

2.9 download text •	5	35

total

7.1	40	499

52.8	162	548

3.6	7	48

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 3b

exclusive and shared non-stop word use frequency

Average and 50%/90% cumulative percentile word frequencies. Non-stop words exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).

set

word frequency

Tim Kaine

1.54 download text •	2	4

Mike Pence

1.66 download text •	2	6

total

3.59 download text •	7	48

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 3

legend

a	b	c

a :: average word frequency

b :: largest word frequency in 50% of content

c :: largest word frequency in 90% of content

bar :: proportion of a:b:c

Table 3

commentary

Sentence Size

Table 4

sentence size

Number of sentences spoken by each speaker and sentence word count statistics. Number of words in a sentence is shown by average and 50%/90% cumulative values for all, stop and non-stop words.

speaker

number of sentences

sentence size

all

stop

non-stop

Tim Kaine

581

13.0 download text •	17	36

7.2 download text •	10	19

6.3 download text •	9	17

Mike Pence

540

14.7 download text •	22	55

8.1 download text •	11	28

7.2 download text •	11	27

total

1,121

15.8	20	48

9.6	11	25

8.7	11	23

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 4

legend

a	b	c

a :: average sentence size

b :: largest sentence size for 50% of content

c :: largest sentence size for 90% of content

bar :: proportion of a:b:c

Table 4

commentary

All further word use statistics represent content that has been filtered for stop words, unless explicitly indicated.

Part of Speech Analysis

In this section, word frequency is broken down by their part of speech (POS). The four POS groups examined are nouns, verbs, adjectives and adverbs. Conjunctions and prepositions are not considered. The first category (n+v+adj+adv) is composed of all four POS groups.

Part of Speech Count

Table 5

part of speech count

Count of words categorized by part of speech (POS).

part of speech

n+v+adj+adv

nouns (n)

verbs (v)

adjectives (adj)

adverbs (adv)

Tim Kaine

3,282 download text •	1,219
43.7%	37.1%

1,810 download text •	683
55.1%	37.7%

824 download text •	374
25.1%	45.4%

516 download text •	277
15.7%	53.7%

132 download text •	50
4.0%	37.9%

Mike Pence

3,555 download text •	1,247
45.1%	35.1%

1,944 download text •	663
54.7%	34.1%

854 download text •	399
24.0%	46.7%

568 download text •	266
16.0%	46.8%

189 download text •	60
5.3%	31.7%

total

6,837 download text •	1,946
44.4%	28.5%

3,754 download text •	1,068
54.9%	28.4%

1,678 download text •	642
24.5%	38.3%

1,084 download text •	445
15.9%	41.1%

321 download text •	80
4.7%	24.9%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 5

legend

a	c
b	d

a :: total number of words for a given POS (all, noun, verb, adjective, adverb, pronoun)

b :: (a) relative to all words by candidate

c :: unique words in (a)

d :: (c) relative to (a)

bar :: proportion of (a-c):c

Table 5

commentary

Part of Speech Frequency

Table 5

part of speech frequency

Frequency of words categorized by part of speech (POS).

part of speech frequency

n+v+adj+adv

nouns (n)

verbs (v)

adjectives (adj)

adverbs (adv)

Tim Kaine

2.69	4	28

2.65	4	44

2.20	3	18

1.86	2	11

2.64	4	28

Mike Pence

2.85	5	35

2.93	6	52

2.14	3	14

2.13	3	15

3.15	7	34

total

3.51	7	48

3.52	7	95

2.61	4	36

2.44	4	23

4.01	9	62

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Table 5

legend

a	b	c

a :: average word frequency

b :: largest word frequency in 50% of content

c :: largest word frequency in 90% of content

bar :: proportion of a:b:c

Table 5

commentary

Part of Speech Pairing

Through word pairing, I extract concepts from the text. The number of unique word pairs is a function of sentence length and is one of the measures of complexity.

Table 6a

part of speech pairing — Tim Kaine

Word pairs (total and unique) categorized by part of speech (POS)

part of speech pairings - Tim Kaine

noun

verb

adjective

adverb

noun

4,599 download text •	3,705
	80.6%

verb

3,862 download text •	3,358
	86.9%

781 download text •	677
	86.7%

adjective

2,288 download text •	1,974
	86.3%

981 download text •	862
	87.9%

298 download text •	263
	88.3%

adverb

594 download text •	538
	90.6%

288 download text •	262
	91.0%

166 download text •	150
	90.4%

29 download text •	27
	93.1%

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Table 6b

part of speech pairing — Mike Pence

Word pairs (total and unique) categorized by part of speech (POS)

part of speech pairings - Mike Pence

noun

verb

adjective

adverb

noun

7,270 download text •	5,625
	77.4%

verb

5,883 download text •	5,002
	85.0%

1,160 download text •	999
	86.1%

adjective

3,468 download text •	2,844
	82.0%

1,392 download text •	1,211
	87.0%

378 download text •	319
	84.4%

adverb

1,245 download text •	1,019
	81.8%

498 download text •	420
	84.3%

292 download text •	256
	87.7%

63 download text •	53
	84.1%

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Table 6c

unique part of speech pairing — candidate comparison

Unique word pairs categorized by part of speech (POS)

unique part of speech pairings

noun (n)

verb (v)

adjective (adj)

adverb (adv)

noun

3,705 download text •	5,625 download text •
	151.8%

verb

3,358 download text •	5,002 download text •
	149.0%

677 download text •	999 download text •
	147.6%

adjective

1,974 download text •	2,844 download text •
	144.1%

862 download text •	1,211 download text •
	140.5%

263 download text •	319 download text •
	121.3%

adverb

538 download text •	1,019 download text •
	189.4%

262 download text •	420 download text •
	160.3%

150 download text •	256 download text •
	170.7%

27 download text •	53 download text •
	196.3%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 6 a,b

legend

a	c
	d

a :: total number of pairs, for a given category (e.g. verb/noun)

c :: number of unique pairs within set (a)

d :: (c) relative to (a)

bar :: proportion of (a-c):c

Table 6c

legend

a	c
	d

a :: unique pairs for Tim Kaine

c :: unique pairs for Mike Pence

d :: (c) relative to (a) (i.e. Mike Pence relative to Tim Kaine)

bars :: (a) and (c)

Table 6

commentary

Exclusive and Shared Usage

This section enumerates words that were exclusive to a candidate (e.g. used by one candidate but not the other). This content provides insight into what the candidates' priorities are and reveals differences in perspective on similar topics.

For a given part of speech, the table breaks down the number of words that were spoken by only one of the candidates or both candidates (intersection). The last row includes words spoken by either candidate (union).

Table 7

exclusive word usage

Total and unique words used exclusively by a candidate, or by both.

part of speech

n+v+adj+adv

nouns (n)

verbs (v)

adjectives (adj)

adverbs (adv)

Tim Kaine

1,056 download text •	699
100.0%	66.2%
15.4%	35.9%

545 download text •	361
51.6%	66.2%
14.5%	33.8%

290 download text •	202
27.5%	69.7%
17.3%	31.5%

198 download text •	149
18.8%	75.3%
18.3%	33.5%

23 download text •	19
2.2%	82.6%
7.2%	23.8%

Mike Pence

1,198 download text •	727
100.0%	60.7%
17.5%	37.4%

595 download text •	361
49.7%	60.7%
15.8%	33.8%

381 download text •	244
31.8%	64.0%
22.7%	38.0%

181 download text •	133
15.1%	73.5%
16.7%	29.9%

41 download text •	29
3.4%	70.7%
12.8%	36.2%

both candidates

4,583 download text •	520
100.0%	11.3%
67.0%	26.7%

2,516 download text •	278
54.9%	11.0%
67.0%	26.0%

914 download text •	131
19.9%	14.3%
54.5%	20.4%

589 download text •	98
12.9%	16.6%
54.3%	22.0%

254 download text •	30
5.5%	11.8%
79.1%	37.5%

total

6,837 download text •	1,946
100.0%	28.5%
100.0%	100.0%

3,754 download text •	1,068
54.9%	28.4%
100.0%	100.0%

1,678 download text •	642
24.5%	38.3%
100.0%	100.0%

1,084 download text •	445
15.9%	41.1%
100.0%	100.0%

321 download text •	80
4.7%	24.9%
100.0%	100.0%

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Table 7c

legend

a	d
b	e
c	f

a :: total number of words in set (e.g. obama \ romney, obama ∩ romney, obama ∪ romney , for a given part of speech

b :: (a) relative to all exclusive words in n+v+adj+adv

c :: (a) relative to all words in n+v+adj+adv

d :: unique words in (a)

e :: (d) relative to (a)

f :: (d) relative to all unique words in n+v+adj+adv

bar1 :: normalized ratio of (a-d):d

bar2 :: absolute ratio of (a-d):d for all POS groups (first column) or POS group (other columns)

Table 7

commentary

Noun Phrase Usage

Noun phrases were extracted from the text and analyzed for frequency, word count, unique word count and richness. Single-word phrases were not counted.

Top-level noun phrases are those without a parent noun phrase (a parent phrase is one that a similar, longer phrase). Derived noun phrases are those with a parent (more details about noun phrase analysis).

The top-level noun phrases can be interpreted as independent concepts. Derived noun phrases can be interpreted as variants on concepts embodied by the top-level phrases.

Noun Phrase Count and length

This table reports the absolute number of noun phrases, which is related to the number of nouns, and their length.

Table 8a

noun phrase count

Counts of noun phrases in words and per noun.

speaker

noun phrase count

all

top-level

Tim Kaine

693 download text •	310
100.0%	44.7%
0.38	0.45

529 download text •	303
76.3%	57.3%
0.29	0.44

Mike Pence

718 download text •	293
100.0%	40.8%
0.37	0.44

557 download text •	288
77.6%	51.7%
0.29	0.43

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 8b

noun phrase length

Average and 50%/90% cumulative length of noun phrases, in words.

speaker

noun phrase length

all

top-level

Tim Kaine

2.29	2	3

2.38	2	4

Mike Pence

2.27	2	3

2.34	2	4

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 8a

legend

a	d
b	e
c	f

a :: number of noun phrases

b :: (a) relative to number of all noun phrases

c :: number of noun phrases per noun

d :: number of unique phrases

e :: (c) relative to (a)

f :: number of unique noun phrases per unique noun

bar :: normalized ratio of (a-c):c

Table 8b

legend

a	b	c

a :: average noun phrase size, in words

b :: largest noun phrase size in 50% of content

c :: largest noun phrase size in 90% of content

bar :: proportion of a:b:c

Table 8

commentary

Exclusive and Shared Noun Phrase Count and length

Table 9a

exclusive and shared noun phrase count

Counts of exclusive and shared noun phrases in words and per noun.

speaker

noun phrase count

all

top-level

Tim Kaine

507 download text •	298
35.9%	58.8%

482 download text •	293
95.1%	60.8%

Mike Pence

547 download text •	279
38.8%	51.0%

513 download text •	278
93.8%	54.2%

both candidates

357 download text •	31
25.3%	8.7%

91 download text •	19
25.5%	20.9%

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 9b

exclusive and shared noun phrase length

Average and 50%/90% cumulative length of noun phrases, in words.

speaker

noun phrase length

all

top-level

Tim Kaine

2.39	2	4

2.41	2	4

Mike Pence

2.34	2	4

2.36	2	4

both candidates

2.03	2	2

2.09	2	3

Fields with • (e.g. 155•) link to data files. Hover over the field to show these links.

Table 9a

legend

a	c
b	d

a :: number of noun phrases

b :: (a) relative to number of all noun phrases

c :: number of unique phrases

d :: (c) relative to (a)

bar :: normalized ratio of (a-c):c

Table 9b

legend

a	b	c

a :: average noun phrase size, in words

b :: largest noun phrase size in 50% of content

c :: largest noun phrase size in 90% of content

bar :: proportion of a:b:c

Table 9

commentary

Windbag Index

The Windbag Index is a compound measure that characterizes the complexity of speech. A low index is indicative of succinct speech with low degree of repetition and large number of independent concepts.

Table 10

windbag index

Windbag Index for each speaker. The higher the value, the more repetitive the speech.

speaker

Windbag Index

index value

index terms

Tim Kaine

386
-39.4%

0.469	0.362	0.377	0.454	0.537	0.379	0.447	0.977
-0.9%	+4.0%	+10.6%	-2.9%	+14.6%	+19.3%	+9.6%	-0.6%

Mike Pence

638
+65.0%

0.473	0.348	0.341	0.467	0.468	0.317	0.408	0.983
+0.9%	-3.8%	-9.6%	+2.9%	-12.8%	-16.2%	-8.8%	+0.6%

Table 10

legend

The Windbag Index is 1/(t1*t2*...*t9) where t1,t2,...,t8 are

t1 :: fraction of words that are non-stop

t2 :: fraction of non-stop words that are unique

t3 :: fraction of nouns that are unique

t4 :: fraction of verbs that are unique

t5 :: fraction of adjectives that are unique

t6 :: fraction of adverbs that are unique

t7 :: fraction of noun phrases that are unique

t8 :: fraction of noun phrases that are top-level

Large individual terms t1...t9 contribute to a smaller index.

The percentage values below the index and each term are relative differences to the other speaker's corresponding term (i.e. 100*(a-b)/b where a is the value for one speaker and b for the other).

Table 10

commentary

Word Clouds

In the word clouds below, the size of the word is proportional to the number of times it was used by a candidate (method details).

Not all words from a group used to draw the cloud fit in the image — less frequently used words for large word groups may fall outside the image.

All Words for Each Candidate

Each candidate's debate portion was extracted and frequencies were compiled for each part of speech (noun, verb, adjective, adverb), with words colored by their part of speech category.

The distribution of sizes within a tag cloud follows the frequency distribution of words. However, word size cannot be compared between clouds, since the minimum and maximum size of the words is fixed.

Debate Word Cloud for Tim Kaine - all words

Debate Word Cloud for Mike Pence - all words

commentary

Exclusive Words for Each Candidate

The clouds below show words used exlusively by a candidate. For example, if candidate A used the word "invest" (any number of times), but candidate B did not, then the word will appear in the exclusive word tag cloud for candidate A.

Words exclusive to Tim Kaine

Words exclusive to Mike Pence

commentary

Part of Speech Word Clouds

In these clouds, words from each major part of speech were colored based on whether they were exclusive to a candidate or shared by the candidates.

The size of the word is relative to the frequency for the candidate — word sizes between candidates should not be used to indicate difference in absolute frequency.