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It allows users to extract meaningful insights from text data without requiring programming knowledge. It
                 offers APIs that can be integrated into applications, but it also provides a no-code platform where users can
                 upload text and get insights without programming.

                         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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