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Real Time Chat Sentiment Analysis For Customer Care
Students Name ID
1 ABU SUFYAN 19101002-185
2 MUHAMMAD ASAD 19101002-068
Supervisor: Ma’am Sadaf Mehmood
Submitted By:
Introduction
The processing of information to determine the opinion of a
customer.
Customer mood or customer sentiment can be queried in real
time.
Good customer service is all about making customers happy.
It helps an agent, know to immediately turn their focus on a
certain customer.
Global Sustainability Development Goal
Industrial Linkage
In the service industry, the key task is to enhance the quality of
services by identifying conversation sentiments.
It helps in to provide valuable insight into the mind of the
customer.
Promotes customers satisfaction, also encourages loyalty.
Problem in Existing System
Untrained agents can not handle different types of queries.
Low NPS score
Traditional quality monitoring solution are not efficient.
Proposed System
Detect client sentiments in real time.
Allowing agents to make adjustment to improve NPS.
Proactive monitoring and alarming.
Real time chat analytics automatically grades every call.
Continuing improving agent performance.
Main Modules
Customer portal Agent portal
APP
Registration
User Registration Agent Registration
Agent Chat with User
User Chat with agent
Tools & Technology
Tools
– Anaconda
– VS code
– Google colab
Technology
Basically we doing the data analysis for real time chat
sentiment analysis of customer care it combines natural language
processing (NLP) and machine learning techniques.
– PYTHON
– HTML,CSS
– React Js
Business Plan
Improve customer service
Conduct a SWOT analysis
Develop a new product
Brand monitoring
Customer support analysis
Customer feedback analysis
Market research

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Real Time chat Sentiment Analysis For Customer Care1122.pptx

  • 1. Real Time Chat Sentiment Analysis For Customer Care Students Name ID 1 ABU SUFYAN 19101002-185 2 MUHAMMAD ASAD 19101002-068 Supervisor: Ma’am Sadaf Mehmood Submitted By:
  • 2. Introduction The processing of information to determine the opinion of a customer. Customer mood or customer sentiment can be queried in real time. Good customer service is all about making customers happy. It helps an agent, know to immediately turn their focus on a certain customer.
  • 4. Industrial Linkage In the service industry, the key task is to enhance the quality of services by identifying conversation sentiments. It helps in to provide valuable insight into the mind of the customer. Promotes customers satisfaction, also encourages loyalty.
  • 5. Problem in Existing System Untrained agents can not handle different types of queries. Low NPS score Traditional quality monitoring solution are not efficient.
  • 6. Proposed System Detect client sentiments in real time. Allowing agents to make adjustment to improve NPS. Proactive monitoring and alarming. Real time chat analytics automatically grades every call. Continuing improving agent performance.
  • 7. Main Modules Customer portal Agent portal APP
  • 10. User Chat with agent
  • 11. Tools & Technology Tools – Anaconda – VS code – Google colab Technology Basically we doing the data analysis for real time chat sentiment analysis of customer care it combines natural language processing (NLP) and machine learning techniques. – PYTHON – HTML,CSS – React Js
  • 12. Business Plan Improve customer service Conduct a SWOT analysis Develop a new product Brand monitoring Customer support analysis Customer feedback analysis Market research