From: Nanyang Technological University in Singapore
December 28, 2020 -- A study by a team
of Nanyang Technological
University, Singapore (NTU Singapore) psychologists has found a
link between extroverts and their word choices.
The finding highlights the need for
stronger linguistic indicators to be developed for use in online personality
prediction tools, which are being rapidly adopted by companies to improve
digital marketing strategies.
Today, marketing companies use
predictive algorithms to help them forecast what consumers want based on their
online behaviors. Companies are also keen to leverage data and machine learning
to understand the psychological aspects of consumer behavior, which cannot be
observed directly, but can provide valuable insights about how to improve
targeted advertising.
For example, an 'extrovert consumer'
might be attracted to marketing messages that match their personality, and
retail brands could then choose to target such consumers by using more extroverted
and creative language to advertise their products.
However, personality prediction tools
available today that are used by marketing firms are not entirely accurate due
to a lack of theoretically sound designs.
Principal investigator of the
study, Associate Professor Lin Qiu
from the Psychology program at the NTU School of Social Sciences said,
"Current machine learning algorithms for personality prediction can seem
like a black box - there are many linguistic indicators that can be included in
their design, but many of them are dependent on the type of computer
application used. This may lead to biases and overfitting, an error affecting
the performance of the machine learning algorithms. This begs the question
– how should we create robust and accurate personality predictions?"
The study found a correlation between extroverts
and their tendency to use certain categories of words. The results showed a
small strength of relationship between extraversion and the use of
"positive emotion words" and "social process words".
Positive emotion words are
defined by psychologists - using text analysis tools - as words that describe a
pleasant emotional state, such as 'love', 'happy', or 'blessed', or that
indicate positivity or optimism, such as 'beautiful' or 'nice'. Social
process words include words containing personal pronouns except 'I',
and words showing social intentions, such as 'meet', 'share' and 'talk'.
"This is the first time a
relationship has been established between extroverts and their tendency to use
the two categories of words. As it is a small correlation, we believe that
stronger linguistic indicators are needed to improve machine learning
approaches, amid rising interest in such tools in consumer marketing,"
Associate Professor Qiu said.
The NTU team said the findings, which
was published in the Journal of Research in Personality in
December 2020, can provide marketers with well-founded linguistic predictors
for the design of machine learning algorithms, improving the performance of
software tools for personality prediction.
How the study was conducted
Previous individual studies reviewed by
the NTU team have shown that extraversion, or the general tendency to
experience positive emotions and enjoy social interactions, is related to the
use of words described by psychologists as "positive emotion" or
"social process" words. But the strength of this reported
relationship has varied substantially between the different studies exploring
it.
To establish the effectiveness of such
linguistic predictors, the NTU team reviewed 37 studies looking at the same
topic to conduct a meta-analysis. Extraversion was determined using
internationally recognized personality type questionnaires.
Moving forward, the NTU research team
will investigate the relationship between extraversion and other word
categories.
While machine learning and predictive
analytics can provide companies and marketers with an added advantage in their
business strategies, more thought must be put into the design of such
analytical models, the NTU research team said.
They hope their work will provide
clarity on the types of words that can help guide the development of more
accurate machine learning tools for personality prediction.
Note to Editors:
Paper titled "A
meta-analysis of linguistic markers of extraversion: Positive emotion and
social process words", published in the Journal of
Research in Personality, Volume 89, December 2020.
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