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"Always have an ear to the customer." The old sales maxim from the analog world is no less important in digital competition. Sentiment-Analysis does just that. It gives companies detailed insights into how customers view their products and their own brand - and it provides answers to questions that are vital for survival.

Emotions are just as important in the digital world as in the analog one. Positive emotions lead to customer loyalty and recommendation within the peer group - in other words, the best marketing tool of all! Negative emotions can accumulate and end in a shitstorm - which brings attention, but it takes an extreme amount of work to use this negative attention for your brand - loosely based on the phrase: There is no such thing as bad advertising.

In order to sift through and evaluate these emotions across channels, mere community management is no longer sufficient above a certain size. In this case, a sentiment analysis must intervene.

What is a Sentiment-Analysis?

Sentiment means feeling, sensation or opinion. These three terms describe quite precisely what is to be captured by a sentiment analysis: The mood of a certain group of people. In practice, sentiment analysis is usually carried out within SOCIAL MEDIA or CHAT and EVALUATION PORTALS. How is my product, the new YouTube video or the funny Facebook post received by customers? Sentiment analysis provides companies and service providers with qualified answers to these questions. With the help of the analysis, the individual posts/opinions of the customers or users are usually grouped into the categories positive, neutral and negative - and thus a reliable mood picture is drawn.

How does this work exactly?

Sentiment analysis is a form of data mining or text mining. It usually involves Natural Language Processing (NLP), i.e. the core statements and emotions are extracted from texts manually or by machine. A common method for this is to deposit classified linguistic sources (dictionaries) in which, for example, terms such as "good", "friendly" and "favorable" are coded as positive and terms such as "unfriendly", "unfortunately" or "late" are coded as negative. A comment such as "friendly service and good service. unfortunately, the product arrived late," would be classified as neutral because it contains two positive and two negative code words each.

In addition, there is the (logically exclusively machine) method of MACHINE LEARNING. For this, software is first fed with sample data for positive and negative expressions of opinion and tonalities in order to subsequently classify unknown texts correctly on the basis of these sources. For this method to deliver reliable results, the source data must be very close to the area of application of the intended sentiment analysis. For example, example data from a soccer fan forum is not a good basis for using it to analyze the product ratings of an electronics company.

Where to apply a Sentiment-Analysis

There are now a whole range of common tools for performing automated sentiment analyses, which simplify and accelerate the entire process, but above all the evaluation. As already mentioned at the beginning, sentiment analysis is mostly used in the context of social media or related forms of communication in which customers or users express opinions about products, services and brands.

It can be used for many business issues and sub-areas and can be applied to:

  • Product ratings
  • Attitude towards the brand/company (image, credibility, etc.)
  • Service and services
  • Customer satisfaction
  • Marketing campaigns
  • Success of social media communication
  • Shitstorm prevention
  • Identification of improvement potential

Benefits and advantages of Sentiment-Analysis

Emotions and moods are decisive factors for purchasing behavior and customer loyalty. A sentiment analysis provides companies with detailed information about the feelings with which customers (users) encounter their own brand. It shows why and in which areas a brand is either loved or even hated by some. These insights offer companies the chance to react promptly and in a targeted manner, ideally turning neutral or negatively disposed customers more and more into fans of their own brand. At the same time, customers who are already convinced can be identified and integrated into MARKETING activities as influencers, for example. And as already mentioned, sentiment analysis can also be used to specifically review sub-areas of the offering, such as customer service, price structure, product quality or a single campaign video. Regular sentiment analyses make trends and changes quickly visible. For example, is trouble brewing on the social media channel after an adjustment to the offer structure? Why is the new product selling much better than its predecessor? And why is satisfaction with our service only mediocre? These are questions that every company naturally wants to know the answers to as quickly as possible.


Sentiment-Analysis offer companies an almost inestimable value. They are more than mere popularity surveys, but reveal very specifically what drives customers and what they want. In short, sentiment analyses give companies the power to win the hearts of their customers.

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