Sentiment analysis with 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 aim is to communicate 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 spontaneous, or the multiplication of channels available to obtain it, one conclusion is clear: the volume of customer feedback to be processed is colossal.

Spontaneous feedback, i.e. online reviews on review sites and social networks, is freely shared by customers to describe their experience with a product or brand. This is a significant source of content for brands to analyze.

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

The benefits of semantic analysis in customer returns management

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

- time (to read all reviews and comments)
- rigorous organization (to correctly record elements of satisfaction and dissatisfaction)
- a solid structure for data analysis
- consistent application of the same analysis methodology by all those carrying out the task

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

This is wheresemantic analysis comes into its own: it can analyze huge volumes of data in a short space of time, freeing up staff for more strategic activities. AI will be much more impartial and consistent in its analysis of comments, providing accurate and reliable results.

Semantic analysis of customer sentiment

Sentiment begins with a tone: positive or negative. It helps us understand whether the customer is satisfied or dissatisfied with the product or experience, whether they're happy or disappointed.

This tonality offers a considerable advantage in quickly identifying the main trends and topics that are causing the most irritation, enabling efforts to be focused on analyzing these key dissatisfying moments. It also helps to quickly identify dissatisfied customers, in combination with a rating, and to resolve the reasons for their dissatisfaction.

When voice is the channel used for customer feedback (customer service, chatbot, IVR...), analyzing the customer's tone can also adapt communication to provide the most satisfactory response possible.

Sentiment analysis for a callbot

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

Callbots are becoming increasingly sophisticated thanks to paralinguistic analysis (measuring voice flow and tone) to adapt the response to make the exchange more human and therefore more qualitative.

Sentiment analysis enables callbots to act with a certain empathy, similar to that of a human advisor on the phone. When the sentiment seems excessively negative, the callbot can quickly transfer the call to a tele-consultant to prevent the situation from deteriorating.

Sentiment analysis is a major asset for understanding the emotions of customers who leave comments or call customer service. It enables us to compare the strengths and weaknesses of the experience, detected through ratings, with those identified by sentiment analysis. It's important to note that these two evaluation methods can reveal significant discrepancies.

It remains to be seen what future technological advances will reinforce this approach, and how sentiment analysis data can be exploited in other channels.