What is Sentiment Analysis, and How Can it Improve CX?

Kevin McNulty at Talkdesk explores how to define sentiment analysis, how it works, and how it can enhance customer experience strategies.

From Twitter/X rants to product reviews to customer support interactions, customer sentiment analysis can extract valuable insights from multiple data sources and inject precision into marketing, sales, and support strategies.

At the intersection of linguistics and artificial intelligence (AI), sentiment analysis allows businesses to tap into the emotions, opinions, and attitudes of their customers.

Using natural language processing (NLP) techniques and machine learning algorithms, sentiment analysis can determine the emotional tone of textual data and make predictions based on patterns and context.

There’s also a wide range of business applications, including customer feedback analysis, reputation management, and product development, making it an essential tool for decision-making and meeting customer expectations in today’s digital marketplace.

What is Sentiment Analysis?

Customer sentiment refers to the emotional tone behind a customer’s feedback – how a customer feels about a company, product, service, or experience.

Whether it’s an enthusiastic review or a cautious complaint, every interaction helps shape the customer’s overall perception of your brand. That’s why customer sentiment is such a powerful indicator of loyalty, trust, and satisfaction.

Sentiment analysis is the method of interpreting and classifying this sentiment at scale. This process involves using NLP to assign emotional weight to customer feedback.

Sentiment analysis doesn’t just process words. These tools understand context and linguistic nuance to derive meaning. This supports organizations in transforming massive amounts of unstructured text, like reviews, surveys, or social media posts, into clear, actionable insights.

Four Sentiment Analysis Tools to Consider

Sentiment analysis tools are software or programs that help businesses understand the emotions and opinions expressed in textual and unstructured voice data. In a contact centre, these can be used to analyze customer interactions and feedback. For example:

• Text analytics software – This software can evaluate written feedback, such as emails, support tickets, chat transcripts, or social media posts, to assess sentiment and identify trends in customer satisfaction.
• Speech analytics tools – These solutions analyze recorded calls to extract insights from voice data, including tone, word choice, and pacing, which can help detect confusion, frustration, or satisfaction.
• AI chatbots – These can analyze sentiment during live chats, helping agents adjust their approach in real time based on the customer’s mood.
• Social media monitoring tools – These tools can track mentions of a company or product on social media and analyze the sentiment of those comments.

When choosing a customer sentiment analytics tool, it should easily gather and dissect sentiment expressions. Ease of use and accuracy are especially important for teams looking to translate raw customer feedback into strategic insight without a steep learning curve.

How Does Customer Sentiment Analysis Technology Work?

Using AI, NLP, and other machine learning algorithms, sentiment analysis models detect patterns in voice (from transcription) or text and classify customer emotions in three or more different categories, positive, negative, neutral, or more fine-grained.

Here are some of the different types of sentiment analysis:

Standard Sentiment Analysis

Standard sentiment analysis relies on AI and NLP to classify unstructured data from sources such as support tickets, phone calls, survey and feedback forms, social media comments, voice transcriptions, and chatbot interactions.

It analyzes specific words that might lead to a sentiment such as “love”, “hate”, “good”, and others to classify the emotions.

Fine-Grained Sentiment Analysis

Fine-grained sentiment analysis is more concrete and specific. Instead of categorizing feedback as positive or negative, it breaks sentiment into tiers, usually as very positive, positive, neutral, negative, or very negative.

This approach helps organizations get a fuller picture of emotional nuance, making it easier to differentiate between overly enthusiastic customers and those who might only be mildly satisfied.

Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) looks at different elements mentioned by a customer and assesses the sentiment attached to each one.

This goes beyond placing interactions in either a positive or negative bucket, but instead provides a well-rounded example of customer feedback. In turn, a business can pinpoint exactly where they are excelling or falling short.

For example, a customer might express satisfaction with product durability but dissatisfaction with shipping times within a single review. ABSA can parse these nuances at scale, delivering more targeted, actionable insights.

Intent-Based Sentiment Analysis

Intent-based sentiment analysis emphasizes the underlying goal or intention that customer comments reflect rather than classifying emotional tone outright.

It’s a means of unlocking what exactly the customer wants or is expecting to occur, whether the desire for assistance, a complaint of some sort, or the notion to enhance a feature.

For example, looking at strings like “this feature doesn’t work,” “I couldn’t log in,” or “you should upgrade your app” allows businesses to identify recurring issues and prioritize improvements instantly.

Emotion Detection

Emotion detection is often associated with customer feedback of frustration, anger, happiness, and other emotions.

The main difference between this and other types of sentiment analysis is that it focuses on things, such as tone and punctuation, to differentiate specific emotions.

For example, the sentence “This product was unbelievable!” might have a different connotation from “This product was unbelievable”.

Reviewed by: Jo Robinson