Analyze call recordings and chat conversations both
internally and externally to gain communication insights.
Analyze call recordings and chat conversations both internally and externally to gain communication insights.
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.
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.
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.
Giving visibility to employee communication with others while working remotely, will help employees stay connected to their team and improve their collaboration with others.
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 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
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