Text Analytics Business Intelligence

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Will the Future of Search be Semantic in 2021?

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Table of Contents
What are Artificial Intelligence and Machine
Learning?

How AI and ML supercharge Text Analytics and NLP

How do Machine Learning Algorithms work in AI
Text Analytics?
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How AI, Machine Learning And Text Analytics
Business Intelligence

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In the era of big data how does a business
harness the insights that lie deep within their
enterprise data? One of the main ways is to apply
artificial intelligence (AI) to text analytics.
AI can help you automate the discovery of the
most important insights that are hidden in your
enterprise data. It can classify it to make it
easy to analyze and apply to real world business
problems. Specifically, AI text analytics can be
used to make the analysis of text data faster,
more accurate, and easier to scale. These data
insights can be used to drive effective strategic
change in operations, marketing, and sales to
improve performance and competitive advantage.
This information can also be coupled with
predictive text analytics to extrapolate on past
and present data patterns to help forecast future
trending.
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What are Artificial Intelligence and Machine
Learning?
Artificial intelligence can be best understood by
elucidating the meaning of its words -
Artificial being something made by humans
rather than emerging naturally, while
Intelligence is the ability to learn and apply
knowledge. Knowledge is information or skills
acquired by experience or training. Simply put,
Artificial Intelligence, or AI, refers to
machines that aspire to copy the basic
characteristics of naturally occuring
intelligence.
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How AI and ML supercharge Text Analytics and NLP
Imagine someone asked you to read a page of any
book and then tell them in 10 words or less what
the page was about. You would probably quickly
read the text and try to pick out the most
important points and then use them to provide
back a brief summary. This is what machine
learning when applied to text analytics does, but
much faster, more accurately and for millions of
documents within seconds. Text analytics is the
automated process of extracting the most high
value, relevant information from documents and
then summarizing them in a way that makes them
visual, easy to analyse and much simpler to apply
to remedy business challenges. AI supercharges
the automated capabilities of computers to read
and understand text by utilizing machine learning
technologies like ML algorithms, NLP text mining,
neural networks, and knowledge graphs.
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How Is a VoC Survey Different From Regular
Surveys?
A regular customer survey helps businesses assess
the satisfaction level of previous customers but
a VoC survey takes analysis to the next level. A
VoC survey helps understand customer pain points
across the entire sales journey and discovers
deeper aspects that are not possible with regular
surveys. Its no secret that customer service is
integral to business success. Around 84 of
consumers admit that customer service is a key
factor in decision-making. Even so, during
difficult times, expectations seem to be higher.

So having insights from a VoC survey gives you an
edge to always be ahead. A Voice of the Customer
survey is a fundamental part of the Voice of the
Customer process. The process involves
understanding the customer needs and how the
customer experience (CX) can be improved. It
follows the entire customer journey, uncovering
various pain points or obstacles along the way.
Each of these discoveries can help improve the
overall level of customer satisfaction.
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How do Machine Learning Algorithms work in AI
Text Analytics?

• Aspect Based Sentiment Analysis - A question that
  many companies ask is how do I apply AI or
  machine learning to help me drive my text or
  predictive analytics?
• The answer is quite simple. All AI/ML processes
  follow the same pattern
• Collect data
• Clean it up
• Train
• Test
• Go back to 2/3 if results are not good enough

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