AI Sentiment Analysis
Analyze call recordings and chat conversations both
internally and externally to gain communication insights.
AI Sentiment Analysis
Analyze call recordings and chat conversations both internally and externally to gain communication insights.
AI Sentiment Analysis
Benefits
Improve
employee experience
Improve employee experience and boost productivity
Companies can understand how their employees feel by analyzing the messages and conversations of their employees which in turn helps cut off the employee turnover rate and increase the organization’s productivity.
Manage the
negative
behavior
Manage the negative behavior of employees
Businesses need to make sure that every employee who is communicating directly with customers is giving an excellent service level. Sphereshield’s AI Sentiment Analysis engine can help businesses identify negative behavior and detect meetings that need some attention due to a large amount of negativity.
Spreading
positive
behavior
Spreading positive behavior to other parts of the company
By viewing employee reports of most negative/positive communication Sphereshield’s AI Sentiment Analysis data helps businesses promote positive behavior in the workplace by comparing employees' performances and encouraging everyone to improve.
Improve the
remote
working
experience
Improve the remote working experience
Giving visibility to employee communication with others while working remotely, will help employees stay connected to their team and improve their collaboration with others.
Improve
customer
satisfaction
Improve customer satisfaction
Understanding the customers’ emotions can empower your employees with the knowledge that can help them better serve the customers. The customer-facing team can therefore offer proactive solutions to increase customer satisfaction.
AI Sentiment Analysis
Use Cases
Customer Success / Support Managers
HR Manager
Employees
How does Sentiment Analysis Work?
AI sentiment analysis combines Natural Language Processing (NLP) and Machine Learning.
Natural Language Processing
NLP uses computer-based methods that analyze the human language used in communications. In order for machines to understand the text and speech used by humans. Some NLP techniques need to be put in place, including Tokenization, Stemming, and Part-of-Speech (POS) Tagging. After the natural language processing is completed the text will be ready for the classification process of machine learning.
Machine Learning
Using existing data, machines are trained to recognize patterns in new data sets to predict the sentiment behind that text and automatically classify it as positive, negative, or neutral
SUPPORTED SERVICES
Microsoft Teams
Cisco Webex
Slack
Coming soon
Zoom
Coming soon