Word Analysis of 2016 U.S. Presidential Debates

Hillary Clinton vs Donald Trump (2nd debate)

9 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

Hillary Clinton

6,004 download text •	1,225
45.7%	20.4%

Donald Trump

7,139 download text •	1,122
54.3%	15.7%

total

13,143	1,786
100.0%	13.6%

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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

Hillary Clinton

981 download text •	664
16.3%	67.7%

Donald Trump

1,045 download text •	561
14.6%	53.7%

both candidates

11,117 download text •	561
84.6%	5.0%

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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

Hillary Clinton

6,004 download text •	1,225
100.0%	20.4%

3,424 download text •	135
57.0%	3.9%

2,580 download text •	1,090
43.0%	42.2%

Donald Trump

7,139 download text •	1,122
100.0%	15.7%

4,127 download text •	146
57.8%	3.5%

3,012 download text •	976
42.2%	32.4%

total

13,143 download text •	1,786
100.0%	13.6%

7,551 download text •	153
57.5%	2.0%

5,592 download text •	1,633
42.5%	29.2%

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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

Hillary Clinton

973 download text •	657
37.7%	67.5%

Donald Trump

1,000 download text •	543
33.2%	54.3%

both candidates

3,619 download text •	433
64.7%	12.0%

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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

Hillary Clinton

4.9 download text •	21	223

25.4 download text •	67	238

2.4 download text •	4	22

Donald Trump

6.4 download text •	24	165

28.3 download text •	77	232

3.1 download text •	5	24

total

7.4	40	420

49.4	139	473

3.4	7	40

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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

Hillary Clinton

1.48 download text •	2	5

Donald Trump

1.84 download text •	2	8

total

3.42 download text •	7	40

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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

Hillary Clinton

348

17.3 download text •	24	46

10.0 download text •	13	27

7.5 download text •	11	22

Donald Trump

651

11.0 download text •	15	38

6.6 download text •	9	24

4.7 download text •	7	17

total

999

15.2	19	44

9.8	12	28

7.7	9	20

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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)

Hillary Clinton

2,407 download text •	1,039
40.1%	43.2%

1,184 download text •	541
49.2%	45.7%

701 download text •	347
29.1%	49.5%

382 download text •	224
15.9%	58.6%

140 download text •	48
5.8%	34.3%

Donald Trump

2,729 download text •	928
38.2%	34.0%

1,281 download text •	510
46.9%	39.8%

741 download text •	267
27.2%	36.0%

529 download text •	251
19.4%	47.4%

178 download text •	55
6.5%	30.9%

total

5,136 download text •	1,565
39.1%	30.5%

2,465 download text •	850
48.0%	34.5%

1,442 download text •	512
28.1%	35.5%

911 download text •	399
17.7%	43.8%

318 download text •	78
6.2%	24.5%

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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)

Hillary Clinton

2.32	3	24

2.19	3	18

2.02	3	24

1.71	2	8

2.92	4	22

Donald Trump

2.94	5	23

2.51	4	17

2.77	5	24

2.11	3	12

3.24	7	22

total

3.28	6	40

2.90	5	26

2.82	6	52

2.28	3	16

4.08	11	44

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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 — Hillary Clinton

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

part of speech pairings - Hillary Clinton

noun

verb

adjective

adverb

noun

3,185 download text •	2,781
	87.3%

verb

3,586 download text •	3,253
	90.7%

959 download text •	864
	90.1%

adjective

1,759 download text •	1,622
	92.2%

977 download text •	897
	91.8%

238 download text •	230
	96.6%

adverb

771 download text •	681
	88.3%

499 download text •	450
	90.2%

227 download text •	216
	95.2%

47 download text •	37
	78.7%

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Table 6b

part of speech pairing — Donald Trump

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

part of speech pairings - Donald Trump

noun

verb

adjective

adverb

noun

2,653 download text •	2,072
	78.1%

verb

2,547 download text •	2,024
	79.5%

596 download text •	492
	82.6%

adjective

1,497 download text •	1,273
	85.0%

764 download text •	661
	86.5%

227 download text •	207
	91.2%

adverb

657 download text •	552
	84.0%

349 download text •	308
	88.3%

190 download text •	179
	94.2%

61 download text •	52
	85.2%

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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

2,781 download text •	2,072 download text •
	74.5%

verb

3,253 download text •	2,024 download text •
	62.2%

864 download text •	492 download text •
	56.9%

adjective

1,622 download text •	1,273 download text •
	78.5%

897 download text •	661 download text •
	73.7%

230 download text •	207 download text •
	90.0%

adverb

681 download text •	552 download text •
	81.1%

450 download text •	308 download text •
	68.4%

216 download text •	179 download text •
	82.9%

37 download text •	52 download text •
	140.5%

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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 Hillary Clinton

c :: unique pairs for Donald Trump

d :: (c) relative to (a) (i.e. Donald Trump relative to Hillary Clinton)

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)

Hillary Clinton

937 download text •	637
100.0%	68.0%
18.2%	40.7%

443 download text •	314
47.3%	70.9%
18.0%	36.9%

316 download text •	221
33.7%	69.9%
21.9%	43.2%

153 download text •	119
16.3%	77.8%
16.8%	29.8%

25 download text •	19
2.7%	76.0%
7.9%	24.4%

Donald Trump

962 download text •	526
100.0%	54.7%
18.7%	33.6%

435 download text •	271
45.2%	62.3%
17.6%	31.9%

239 download text •	143
24.8%	59.8%
16.6%	27.9%

244 download text •	142
25.4%	58.2%
26.8%	35.6%

44 download text •	27
4.6%	61.4%
13.8%	34.6%

both candidates

3,237 download text •	402
100.0%	12.4%
63.0%	25.7%

1,490 download text •	201
46.0%	13.5%
60.4%	23.6%

834 download text •	102
25.8%	12.2%
57.8%	19.9%

422 download text •	76
13.0%	18.0%
46.3%	19.0%

235 download text •	25
7.3%	10.6%
73.9%	32.1%

total

5,136 download text •	1,565
100.0%	30.5%
100.0%	100.0%

2,465 download text •	850
48.0%	34.5%
100.0%	100.0%

1,442 download text •	512
28.1%	35.5%
100.0%	100.0%

911 download text •	399
17.7%	43.8%
100.0%	100.0%

318 download text •	78
6.2%	24.5%
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

Hillary Clinton

398 download text •	232
100.0%	58.3%
0.34	0.43

348 download text •	225
87.4%	64.7%
0.29	0.42

Donald Trump

447 download text •	219
100.0%	49.0%
0.35	0.43

376 download text •	207
84.1%	55.1%
0.29	0.41

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Table 8b

noun phrase length

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

speaker

noun phrase length

all

top-level

Hillary Clinton

2.33	2	3

2.37	2	3

Donald Trump

2.30	2	3

2.35	2	4

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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

Hillary Clinton

362 download text •	225
42.8%	62.2%

341 download text •	223
94.2%	65.4%

Donald Trump

410 download text •	211
48.5%	51.5%

369 download text •	204
90.0%	55.3%

both candidates

73 download text •	18
8.6%	24.7%

14 download text •	5
19.2%	35.7%

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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

Hillary Clinton

2.36	2	3

2.38	2	3

Donald Trump

2.33	2	4

2.36	2	4

both candidates

2.00	2	2

2.00	2	2

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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

Hillary Clinton

214
-71.5%

0.430	0.422	0.457	0.495	0.586	0.343	0.583	0.970
+1.9%	+30.4%	+14.8%	+37.4%	+23.6%	+11.0%	+19.0%	+2.6%

Donald Trump

751
+250.5%

0.422	0.324	0.398	0.360	0.474	0.309	0.490	0.945
-1.8%	-23.3%	-12.9%	-27.2%	-19.1%	-9.9%	-16.0%	-2.5%

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 Hillary Clinton - all words

Debate Word Cloud for Donald Trump - 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 Hillary Clinton

Words exclusive to Donald Trump

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.