Artificial Intelligence with Heart: Improving Customer Experience through Sentiment Analysis

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Artificial Intelligence with Heart Improving
Customer Experience through Sentiment Analysis
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• You might not think that emotion and artificial
  intelligence (AI) belong in the same sentence.
  However, a research field has emerged that uses
  AI to help marketers understand the emotional
  reactions that people have to the news as well as
  their experience with movies, products,
  restaurants, and other forms of consumerism.

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• Emotional AI, also known as sentiment analysis,
  is a type of machine learning that analyzes
  negative, neutral, and positive views from a
  written report and uses it to gauge and
  understand customer reactions.

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• Sentiment analysis is useful to track
  psychological trends, analyze social media, and
  review the results of market research. Its
  software scans reviews, ratings, social media
  posts, and articles to pick up on the sentiments
  of the people completing them. This allows a
  marketers clients to form an aggregate from the
  ratings and use it to improve their service to
  customers.

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• Sentiment analysis uses technology that employs
  both linguistic algorithms and natural language
  processing to assign values to various customer
  responses. At the same time, machine learning
  accesses sets of data to uncover the most
  relevant trends that have occurred over a set
  time.

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• This requires a significant amount of planning.
  You must ensure that you use the right algorithms
  to capture the most useful information. Its also
  important to analyze the proper phrases so you
  can convert your findings into improved
  experiences, products, and services. This
  technology makes it possible to identify the
  features that people prefer the most as well as
  those that tend to make them feel frustrated.

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Its Complicated to Gauge Emotions

• Humans are complicated and fascinating beings,
  which means that sentiment analysis can be
  challenging to get right. People often dont
  speak directly and hide criticisms into
  statements that appear positive on the surface.
  For example, someone could say that a product
  wasnt bad. Does that mean its good or needs
  improvement?

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• Since this is such a new field, marketers are
  taking a variety of approaches in using it. At
  the same time, sentiment analysis is maturing. In
  the past, people have approached it using
  something called bag of words. This means that
  they create a list of all words used by customers
  as well as how often they used them. Such a
  method doesnt consider the order of words at
  all. That means it would score someone rating a
  product as not bad as negative feedback.

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• As the technology has advanced, users now rely
  on neural networks that employ a technique called
  long short-term memory or LSTM. This enables them
  to condense an entire written sentence into a
  single vector. It takes the order of words into
  account when deciphering what a sentence means.

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• Businesses that serve customers directly find it
  too overwhelming to analyze all of their feedback
  manually. Using sentiment analysis as well as
  considering the context makes it possible to
  catch services issues and take corrective action
  as early as possible. The algorithms of machine
  learning can analyze large amounts of data and
  then quickly learn and perform certain tasks.

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• The program sifts through the priorities that
  you previously determined to make it as accurate
  as possible. As this technology continues to
  expand, businesses that utilize it can expect to
  see a significant competitive advantage.
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