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.
While keyword-based sentiment analysis looks for individual words, simple language models are slightly more advanced and accurate, as they can look at entire sentences. SLM sentiment analysis identifies all the positive and negative statements from a call transcript and then aggregates them to produce an average for the final score.
However, simple language models still look at sentences without the full context of the conversation. For instance, if a customer spends most of a call venting about a frustrating issue, but ends the call satisfied with the resolution they received, a SLM sentiment analysis tool will still mark it as a “Negative” call, due to the negative statements outweighing the positive.
You can think of a SLM as a halfway point between keyword-based sentiment analysis and large language models, which brings us to…
Large Language Model
A large language model is a probabilistic model of natural language trained on massive amounts of data that can understand and generate phrases based on the text it was trained on. You can think of it as a complex auto-complete feature that can create sentences based on a probable series of words.
Sentiment analysis using a large language model goes far beyond the previous examples, as it can understand the entire context of a conversation through the transcript. They can also pick up on nuances such as sarcasm, providing accurate insights into conversations.
LLM-based sentiment analysis depends heavily on the sentiment analysis prompt provided to the AI, which allows contact centers to define positive and negative calls quickly and easily. For example, a sales contact center can classify calls where a deal is closed as positive, while calls where an agent fails to close a deal are negative.
Previous sentiment models either lacked this customization option or needed significant work to reach it. MiaRec’s AI Prompt Designer enhances this process, enabling users to create and test prompts in natural language for precise sentiment analysis.
As the technology behind sentiment analysis continues to advance, companies like MiaRec are unlocking increasingly powerful, accurate, and customizable ways to understand how customers are feeling and how successful your calls are. It’s clear that using an LLM for sentiment analysis provides the most clear and precise view into each of your calls, helping you to create an excellent contact center experience for both your customers and your agents.
