Page 251 - Ai_C10_Flipbook
P. 251
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.
Natural Language Processing 249

