It is very important for a company to measure customer satisfaction. Today, it is possible to do sentiment analysis by using artificial intelligence and detect if a customer liked or not your products!
Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media. It can be done by analyzing face expression, audio files or written content.

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Listening to your customers helps you understand how they perceive your brand and offers insights into market trends and opportunities for improvement.
To keep track of what customers say about your brand, you need adaptive.sentiment.analysis, which helps you automatically identify the emotional tone in comments and gain fast, real-time insights from large sets of customer data.


Deploy anywhere

Run adaptive.sentiment.analysis
wherever your data resides.

Real time detection

Detect in real time human emotions like happiness, sadness, surprise, neutral from images, videos, audio or written text.

Built-in security

Rely on enterprise
grade security and privacy
applied to both your data and any trained models.

High Accuracy

Our tool is based on the most recent machine learning algorithms that can analyze sentiment with human-like accuracy.


Perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear.
 AI-based facial expression analysis mimics our human coding skills quite impressively as it captures raw, unfiltered emotional responses .


To better understand your customers’ view of your products & services, you can implement audio sentiment analysis into your research channels. Our algorithm automatically transform an audio file into written text that can be further analyzed.
Sentiment analysis on conversational audio data can be classify in three categories: positive, negative or neutral.


Sentiment analysis performed on textual data is a natural language processing (NLP) technique used to help businesses monitor brand and product sentiment in customer feedback, or to a better understanding of customer needs.
Using AI, our solution can pick up cues that indicate the sentiment attached to a segment of text, showing whether the content is happy, sad, or evokes any other sentiment the model is trained to recognize.

How does it work?

Use case scenarios

Protect brand reputation

Find negative comments and talk to their authors before they turn into a PR crisis.

Find marketing insights

Learn what your customers think about your products and adjust your offer to meet their needs.

Improve customer service

Using real-time data from customer support calls companies are able to ensure employees are following proper customer service etiquette and improve upon customer-client relations. 

Social media monitoring

Businesses can monitor metrics such as brand mentions and sentiments associated with each mention.



Several finance giants across the globe have already put their steps forward to invest in business units focused on AI and ML implementations that could predict their clients’ sentiments to market dynamics. 


The hospitality industry can use artificial intelligence-powered sentiment analysis mainly to process hotel reviews and understand what customers like the most and which aspects can be improved.


This sector can use sentiment analysis to discover how consumers feel about their brand and identify trends that might affect their market in the future. 


Telecommunications is another sector where the ability to handle customer complaints and requests is crucial. Thus, this companies can convert their stored calls into text and used AI sentiment analysis to calculate sentiment scores and identify instances where customers sustained a negative sentiment.

Get in touch with one of our colleagues to find out more about our products and how we can customize them to fit your business needs.

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Run adaptive.


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