Sentiment analysis is the interpretation and classification of emotions within voice (call) and text data (call transcription). With this process you can identify the customer’s sentiment toward products, brands or services in online conversations.

At Call Sumo we use sentiment analysis in determining the tone of the conversation for both the customer and the representative. We generate a “score” as a whole to identify which calls should be reviewed for training, and identify which conversations went well!

Sentiment Analysis and Math

Typically, you can read a post and understand whether its author feels positive or negative about the topic. Humans have a good understanding of the English language, while computers run on a different algorithm since it is not natural for computers to understand how spoken language works.  This makes it necessary to use mathematical equations, it is the computer’s native language. Furthermore, there is simply no way for a computer to determine whether a person intended to convey anger, joy, frustration, or other emotions if it doesn’t have context for the words.

sentiment analysis

Sentiment Analysis can solve this issue. With this process the most useful phrases and keywords can be recognized by the computer, which will then help classify and determine the emotional intent of the people involved in a conversation.

One of the simplest techniques used by Sentiment Analysis is keyword spotting. In call recordings the transcription is used as basis for words that obviously express a positive or negative emotion. “Happy” and “disappointed” are two examples. Of the various data from call transcriptions, each has a library containing phrases and words scored as negative, neutral, or positive.

This technique isn’t without its drawbacks. It can’t differentiate when one user expresses two very different emotions. The solution is to create an algorithm that reads words such as “and” or “but” as clues for different types of sentiment expressed. Each sentence in a call transcription is considered and receives a separate score.

Machine learning algorithms can never be perfect and the same is true of sentiment analysis. And because language is complex, the score we get is generally accurate but not perfect.

Why Use Sentiment Analysis?

Sentiment analysis is the best way for you to understand customer expectations and experiences. Having your calls recorded and analyzed can give you the best insight into how you can improve your business.

Here’s what you can expect from Sentiment Analysis:

  • Better targeting of your market
  • Track customer emotion and sentiment over time
  • Find out how handling of a call affects your business

If you’re looking for more insights on your customers and your business calls, you can’t go wrong with sentiment analysis. Companies can use it to adjust marketing strategies based on customer response making it one of the best tools for marketing.