One of the biggest impacts artificial intelligence (AI) can have on a contact center is improving customer satisfaction. When properly utilized, AI can empower agents to efficiently aid callers, leading to a better customer experience (CX).
On top of that, we can more easily track customer satisfaction thanks to improvements in sentiment analysis.
With that in mind, let’s take a closer look at sentiment analysis, the role large language models (LLMs) play in improving sentiment analysis tools, and how companies like MiaRec are changing how we look at calls.
What is Sentiment Analysis?
Sentiment analysis is a tool that uses natural language processing (NLP) to analyze calls and transcriptions to understand how the callers are feeling, how agents performed, and if the call was resolved properly.
Typically, sentiment analysis tools sort calls into one of three categories:
- Positive (indicating satisfaction, enthusiasm, and appreciation)
- Negative (indicating frustration, disappointment, or dissatisfaction)
- Neutral (no strong feelings one way or the other)
Classifying calls into these categories gives businesses a quantifiable way to measure customer interactions and gain valuable insights into the CX. This helps them identify trends, improve agent performance, and make informed business decisions to provide callers with the best support possible.
Types of Sentiment Analysis
Not all sentiment analysis tools work the same way. According to MiaRec, who has used different technologies to provide AI-based analytics for many years, there have been several generations of sentiment analysis, which include:
Keyword-Based
Keyword-based sentiment analysis (commonly referred to as “rule-based”) scans transcripts for specific keywords from a predefined list of “positive” and “negative” terms. These keywords are assigned scores, typically based on how positive or negative they are, which are used to determine overall customer satisfaction. For instance, a customer saying “great” may be worth a score of +5, while a customer cursing would be -10.
However, this method is the least accurate, as it looks for the words and terms regardless of context and cannot pick up on verbal cues. For instance, if a customer says, “well that’s just great,” most would understand it to be sarcastic, but the sentiment analysis tool would still pick up the word “great” and assume it’s a positive statement.
Simple Language Model
Simple language models (SLMs) are pre-trained tools designed to detect positive and negative sentiments. They can be customized for a company’s specific business use case, but doing so is a complicated, cumbersome task, so most organizations rely on the default settings.