The document discusses a study conducted by a text analytics group to determine if certain personality traits exhibited in presidential candidate speeches can influence election outcomes. The group analyzed over 1,500 speeches from seven candidates across six US elections. They extracted measures of the Big Five personality traits and usage of past, present, and future verbs to indicate vision versus past comparisons. Optimism/pessimism was also measured. Statistical models showed that speeches exhibiting optimism, a forward-looking vision, and specific personality traits were more likely to result in greater vote percentages and election victories, both at the state and national level. The results demonstrate that presidential elections involve personality battles and that certain traits in campaign speeches are rewarded by voters.

1. The Presidential Race: A look at election
speeches and what personality wins
Our text analytics group recently enjoyed a coffee break during a light monsoon. We
discussed the idea that leaders have a unique personality trait that makes them lead. It
did not take much time to convince ourselves to carry out a pet project in our free time
to prove it right or otherwise. We are data science geeks, and it is in our blood to extract
intelligence from data. So we began our hunt for data.
The U.S. presidency race was an ideal choice, because nothing is bigger than the US.,
President as a leader in today’s world. We discovered a niche worthwhile project
running at University of California, Santa Barbara called, The American Presidency
Project. Established in 1999, they claim to host the most comprehensive archive of
American Presidencies with more than 100,000 documents, including the campaign
speeches of presidential candidates of six U.S. elections. That was it, the data was in
our hands, now it was time to tame it!
Within an hour, we got about 1,500 speeches across all states for seven candidates
during six presidential elections. It was time to extrapolate interesting features from
these speeches. With a hypothesis in mind that certain characteristics of spoken text
impact people in some way and influence their vote towards a presidential candidate,
we set out to prove that features of spoken text by presidential candidates influence the
voting outcomes.
We extracted quantified measures of Big Five Personality Traits (Extraversion,
Emotional Stability, Agreeableness, Conscientiousness, and Openness to Experience)
using a machine learning model based on Mairesse et. al (2007) model. Use of past,
present, and future verbs in these speeches tells about futuristic vision versus past
comparative behaviors of candidates. These were quantified by examining types of
verbs. Finally, we came down to measuring optimism/pessimism in speeches using a
verified list of dictionaries to index these measures.
Obama and Kennedy had a strikingly unique winning characteristics as compared to the
losing candidates in all three elections, shown below. However, this doesn’t prove
anything until we run a statistically valid model on the same.

3. Next, we looked into what helps a candidate win elections. We ran four models with four
assumptions:
1. State – Votes: Speech in a particular state affects % of votes of that state only
(Multi-Variate Linear Regression).
2. State – Outcome: Speech in a particular state affects winning outcome of that
state only (Logit Regression).
3. Nation – Votes: Speech in a particular state affects % of votes across the
nation.
4. Nation – Outcome: Speech in a particular state affects winning outcome across
the nation.
The outcomes below indicate three things very clearly:
The results demonstrate that presidential elections are also personality battles.
Carefully drafted campaign speeches, optimism and forward looking candidates are
rewarded by voters, while pessimism and bringing up past are punished. Similar
applications of text data analysis can be replicated in the fields of sales calls, customer
service calls and corporate leadership speeches.
Text analytics is as powerful a tool for political parties as it is for brands. It is real data,
in real time, from real people that can give you the intelligence to vote. When you look

4. at words as data, you can draw interesting insights from various intersections of
transcribed spoken text that lead to new realms of possibilities. Perhaps the day is not
far when a self-learning machine will be able to compose a speech on its own and
deliver it, without the audience knowing.