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Natural Language Processing: Use Case


              Customer Feedback Analysis
              Problem
              A company wants to analyse customer feedback from surveys, emails, and reviews to identify common complaints
              and improve service quality.
              Solution
              To identify the source of the problem and find an optimal solution using NLP, we should follow the given steps:
               Step 1    Data Collection: Collect feedback data from multiple sources (e.g., survey results, customer emails,
                        social media posts).
               Step 2   Preprocessing: Data to be pre-processed by following the given instructions:
                        •  Clean the text (remove special characters, stopwords, etc.).
                        •  Tokenise the text into words or phrases for analysis.
                        •  Normalise data (e.g., lowercase, stemming/lemmatisation).
               Step 3    Sentiment Analysis: Use NLP tools like spaCy, NLTK, or no-code platforms like MonkeyLearn to detect
                        positive, negative, or neutral sentiment in feedback.
               Step 4    Topic Modelling: Apply topic modelling techniques to identify common themes or issues mentioned
                        in feedback (e.g., "delivery delays" or "product quality").
              Outcome
              The company uncovers that most complaints are about delayed deliveries in a specific region. Based on these
              insights, they optimise logistics for that region, reducing complaints by 30%.

                       Sentiment Analysis


              Sentiment analysis is a technique within Natural Language Processing (NLP) that helps determine the
              emotional tone behind a piece of text. The goal is to analyse whether the text expresses a positive, negative,
              or neutral sentiment.
              This can  be applied to various forms of communication, such as customer reviews,  social media posts, and
              feedback messages, to gain insights into how people feel about a specific topic, product, service, or brand.
              1.  Customer Service
                   Customer sentiment  analysis  helps  in the automatic detection of emotions  when customers interact with
                  products, services, or brands.
              2.  Voice of the Customer
                   Voice of the customer analysis helps to analyse customer feedback and gain actionable insights from it. It
                  measures  the  gap  between  what  customers  expect  and  what  they  actually  experience  when  they  use  the
                  products or services.



















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