Artificial Intelligence and machine learning might be the technologies
creating sensation across several industries in today’s world, but for the
communication sector, they aren’t alone. It is’ the natural language processing
(NLP) that is supporting them and has even made their essence relate to the
world of Public Relations (PR)
and communications.
Simply defined, NLP refers to the ability of a computer program to
understand, analyze and revert to the human language as per their literal
rules. At present, there are several innovations like Alexa, Siri and Google
Play that has made commands like “Alexa, add milk to my shopping list” and
“Siri, call Mom” possible. It took almost half a century and several
technological innovations to bring the machine learning up to this stage and
hopefully much more is about to come.
But, now the question is, how can we tap into the power of NLP and AI to
serve better in the PR and communication world?
Natural language processing otherwise includes a broad scope of abilities
for the betterment of humankind, but it’s their power of identifying audience
sentiments that have worked to the benefit of communicators. It has empowered
the PR professionals in planning and executing impactful earned media programs
in a way that has never been done before.
Earlier for technologies, communication only involved simple logic-based
algorithms that identified words as either positive or negative and left many
terms as unclassified. But today, the time and technology have transformed
completely changed; thus, equipping the automated systems with the power to
adequately interpret the nuances of human communication and deliver the context
and attribute of the words true to their meaning.
With a crowd and noise of too many in the media world, the idea to lead
with PR strategies is not limited to the number of coverage but is also to
identify the stories and news issues that will get the desired impact on the
target audience. All-and-all this is a time-taking and tedious process but with
NLP engine built in the automated systems, it becomes a quick work to easily
identify sentiments from the string of texts brought in through any relevant
channel.
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