Sentiment Analysis Using Semantics: Understanding Customer Emotions

The growing need for semantic analysis

The 21st century is the age of sharing: my life on Facebook, my opinion on Twitter, my experience on Google… The goal is to share information about what I’m doing, but above all, whether it was a satisfying, expected, or disappointing experience.

Whether it’s customer feedback—whether solicited or unsolicited—or the growing number of channels available for gathering it, one thing is clear: the volume of customer feedback to be processed is enormous.

Organic feedback—that is, online reviews on review sites and social media—is freely shared by customers to describe their experience with a product or brand. It is a significant source of content for brands to analyze.

According to a Capterra study, 96% of respondents prefer written reviews to reviews that consist solely of star ratings.

The Benefits of Semantic Analysis in Customer Return Management

Today, semantic analysis is essential for understanding the customer experience. To fully grasp it, you need to:

- time (to read all reviews and comments)
- rigorous organization (to properly document factors contributing to satisfaction and dissatisfaction)
- a robust framework for data analysis
- consistent application of the same analytical methodology by everyone performing this task

However, it is clear that humans are not always able to perform all these tasks reliably and consistently.

This is wheresemantic analysis really comes into its own: it can analyze massive amounts of data in a short amount of time, freeing up employees to focus on more strategic tasks. AI will be far more impartial and consistent in its analysis of comments, delivering accurate and reliable results.

Customer sentiment analysis using semantics

A sentiment starts with a tone: positive or negative. It helps us understand whether the customer is satisfied or dissatisfied with their product or experience, and whether they are happy or disappointed.

This approach offers a significant advantage in quickly identifying key trends and the issues that cause the most frustration, thereby allowing you to focus your efforts on analyzing these critical points of dissatisfaction. It also helps you quickly identify dissatisfied customers—in combination with a rating system—and address the reasons for their dissatisfaction.

When voice is the channel used for customer feedback (customer service, chatbots, IVR, etc.), analyzing the customer's tone can also help tailor the response to provide the most satisfactory answer possible.

Sentiment analysis for a callbot

The "direct" communication channel requires a more detailed analysis to adjust the callbot's response in real time.

Callbots are becoming increasingly sophisticated thanks to paralinguistic analysis (which measures speech rate and tone) to tailor their responses in a way that makes the interaction feel more human and, therefore, of higher quality.

Sentiment analysis enables callbots to respond with a degree of empathy, similar to that of a human agent on the phone. When sentiment appears to be excessively negative, the callbot can quickly transfer the call to a human agent to prevent the situation from escalating.

Sentiment analysis is a key tool for understanding the emotions of customers who leave reviews or contact customer service. It allows you to compare the strengths and weaknesses of the experience—as reflected in ratings—with those identified through sentiment analysis. It is important to note that these two evaluation methods can reveal significant discrepancies.

It remains to be seen which future technological advancements will strengthen this approach and how data from sentiment analysis can be leveraged across other channels.