Sentiment analysis uses machine learning algorithms to automatically gauge conversations for their sentiment. Before we jump into algorithms, let’s consider the different systems a conversation can be analyzed by.
There are three main sentiment analysis networks for analyzing sentiment. Each system is infinitely refinable. You can apply any combination of these systems to the content you are analyzing for increased understanding.
The first sentiment analysis network is polarity. Polarity is used to determine how closely sentiment falls on a scale of two extremes. The three basic points within this system are positive, negative, and neutral. You can make your scale more precise with more points in between the two extremes. The second is emotion detection.Sentiment analysis software draws emotional meaning from conversations by using lists that target and count words associated with certain emotions. In emotion detection, words are categorized by which emotion they are associated with.
Sentiment analysis is useful because it gives contact centers the ability to qualify and quantify customer sentiment that is embedded in conversations. When customer service teams can gauge the feelings of their customers, they take important steps toward the overall optimization of their customer experience.
More Sentiment Analysis Resources
Understand how call sentiment is affecting business outcomes by using SpeechIQ’s reporting tools to overlay sentiment scores with relevant call metadata.
Quickly extract meaningful performance insights from multiple data sources, track work flows, coach, and communicate with agents through a single, unified interface.
The best speech analytics tools can be configured to provide you with custom alerts and notifications. These can be completely customizable by you, set up to notify the most appropriate people on your team, and alert you to risks in close to real-time.
For those that have already implemented speech analytics, or for those considering it, here are 10 best practices to help you get the most out of the technology. 10 Best Practices to Get the Most Out of Speech Analytics
Sentiment analysis tools provide you insight into what your customers feel toward your organization. They can also help you determine what your customers need. This is a key part of understanding how you can best serve your customer.
Artificial intelligence, text analysis, machine learning, and natural language processing have come a long way in the past few years. These technologies have turned sentiment analysis into a precise way to determine the emotional tone of conversations. It is automatic and requires little input once it has been configured.