The Rise of Sentiment Analysis: How AI is Shaping Modern Contact Centers

The Rise of Sentiment Analysis: How AI is Shaping Modern Contact Centers

One of the biggest impacts artificial intelligence (AI) can have on a contact center is improving customer satisfaction. When used correctly, AI can enable agents to efficiently assist callers, resulting in a better customer experience (CX).

In addition, we can monitor customer satisfaction more easily thanks to improvements in sentiment analysis.

With that in mind, let’s take a closer look at sentiment analysis, the role that large linguistic models (LLMs) play in improving sentiment analysis tools, and how companies like MyRec They are changing the way we think about calls.

What is sentiment analysis?

Sentiment analysis is a tool that uses natural language processing (NLP) to analyze calls and transcripts to understand how callers feel, how agents behaved, and whether the call was resolved correctly.

Typically, sentiment analysis tools classify calls into one of three categories:

  • Positive (indicates satisfaction, enthusiasm and appreciation)
  • Negative (indicates frustration, disappointment or dissatisfaction)
  • Neutral (no strong feelings one way or the other)

Categorizing calls into these categories gives companies a quantifiable way to measure customer interactions and gain valuable CX insights. This helps them identify trends, improve agent performance, and make informed business decisions to provide callers with the best possible support.

Types of Sentiment Analysis

Not all Sentiment analysis tools They work the same way. According to MiaRec, which has been using 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 “rules-based”) analyzes 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 example, a customer who says “great” might be worth a score of +5, while a customer who swears might be worth a score of -10.

However, this method is the least accurate, as it looks for words and terms regardless of context and fails to pick up on verbal cues. For example, if a customer says, “Well, that’s just awesome,” most people would interpret it as sarcastic, but the sentiment analysis tool would still detect the word “awesome” and assume it’s a positive statement.

Simple language model

Simple Language Models (SLMs) are pre-trained tools designed to detect positive and negative sentiment. They can be customized for a company’s specific business use case, but doing so is a complicated and cumbersome task, so most organizations rely on the default settings.

While keyword-based sentiment analysis looks for individual words, simple language models are a little more advanced and accurate, as they can examine entire sentences. SLM sentiment analysis identifies all 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 example, if a customer spends most of a call venting about a frustrating issue, but ends the call satisfied with the resolution, an SLM sentiment analysis tool will still flag it as a “Negative” call, since negative statements outweigh positive ones.

You can think of an SLM as a middle ground between keyword-based sentiment analysis and large-scale language models, which brings us to…

Large language model

A large language model is a probabilistic natural language model trained on huge amounts of data that can understand and generate sentences based on the text it was trained on. You can think of it as a complex autocomplete feature that can create sentences based on a likely set of words.

Sentiment analysis using a large language model goes far beyond the previous examples, as it can understand the full context of a conversation through transcription. It can also pick up nuances like sarcasm, providing accurate conversational insights.

LLM-based sentiment analysis relies heavily on the sentiment analysis prompt provided to the AI, which allows contact centers to quickly and easily define positive and negative calls. 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 didn’t have this customization option or required a lot of work to achieve it. MiaRec’s AI Prompt Designer improves this process by allowing users to create and test natural language queries for accurate 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 feel and how effective your calls are. It’s clear that using a sentiment analysis LLM provides the clearest, most accurate view of each of your calls, helping you create a great contact center experience for both your customers and your agents.

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