Intelligent summary of customer reviews

Context: customer opinions (reviews)

In the era of digital commerce, consumer feedback has become an essential pillar for measuring satisfaction and identifying avenues for improving a product. Customer reviews are goldmines of information., reflecting real experiences and sincere opinions. However, the sheer volume of reviews scattered across multiple e-commerce platforms can pose a significant challenge: the time and effort spent sifting through them and drawing relevant insights can be daunting for product teams.

Aware of this problem, we developed an innovative solution for one of our clients: an advanced feedback summary tool. Using state-of-the-art natural language processing (NLP) algorithms, our tool manages to accurately filter relevant content from customer feedback.

But how exactly does it work? Our system relies on Large Language Models (LLM), pre-trained on large textual datasets. These are neural networks that have an incredible ability to understand nuance and context, identifying key terms and sentiments to provide a consistent and reliable summary of feedback on a specific product.

The power of these pre-trained models lies in their ability to produce high-quality insights even froma limited number of labeled data.

Our tool is therefore a solution of choice for companies wishing to accelerate and optimize their feedback analysis process. By distilling concise, meaningful summaries from a sea of ​​feedback, it provides businesses with the ability to respond quickly and continually improve their products, ensuring better alignment with customer expectations and a sustainable competitive advantage.

Intelligent Synthesis Artificial Intelligence Solution


" I have been very disappointed par the customer experience of this merchant. My package arrived damaged and no one responded to my complaint. I love this perfume, I take it all the time, The smell woody is bewitching et warm without doing old-fashioned. The bottle is stylish and I find that it makes a decorative element nice In my bathroom.

Next time, I will go to the store, even ifi le potpourri perfumeries me makes you feel nauseous.”


Keep only what concerns the perfume: I love it this perfume, I take it all the time, The smell woody is bewitching et warm without doing old-fashioned.


General feeling
  • I love
Perception of smell
  • Woody
Perceived emotion
  • Bewitching
  • Warm
  • Dog old-fashioned

To meet the specific needs of analyzing reviews regarding the smell of a perfume, the algorithm we designed must accomplish the following tasks:

  1. Focus on relevant comments :
    Recognize and isolate segments of text in reviews that specifically mention the scent of the perfume. The algorithm should be able to distinguish these segments from comments that address other aspects such as packaging or quality of customer service.

  2. Management of negations :

    Correctly identify the use of negative structures in language. Phrases like “doesn’t smell good” or “doesn’t last” reverse the meaning of sentences and are crucial to accurately interpreting the sentiment of the review. 

3. Classification of emotions :

Evaluate the emotional tone associated with comments about the scent – ​​whether positive, negative, neutral, or other, among a range of possible emotions such as joy, disappointment, wonder, etc. 

4. Categorization of odor types :

Identify and classify odor descriptors used in reviews. Whether the review mentions a floral, woody, musky, sweet scent, or any other olfactory typology, the algorithm must be able to recognize and group these terms to provide a coherent analysis of customers' olfactory preferences

5. Summary of general feeling :

Compile the collected information to form an overall summary of customer sentiment toward the scent of the perfume. This summary should reflect not only the distribution of sentiments expressed but also provide an overview that highlights trends, recurring patterns, or notable exceptions.

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