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Every day, buyers leave reviews, helpful comments, and personal experiences online—whether about a product, service, process (like purchasing or delivery), or a brand. But how can companies determine whether this feedback is mostly positive or negative? That’s where sentiment analysis comes in. In this article, we’ll explore how sentiment analysis helps brands aggregate, analyze, and interpret customer emotions across various channels.
Sentiment analysis is a method used to evaluate customer reviews, comments, and related data to determine the emotional tone expressed—whether it’s positive, negative, or neutral.
This allows companies to understand whether customers had a good experience with a product, service, or brand—or if something important was missing.
Comments like “Great quality and super comfortable!” are interpreted as positive sentiment. In contrast, something like “The fabric feels cheap” signals negative sentiment and encourages product improvements.
It’s important to note that a single comment can contain multiple sentiments, making analysis more complex.
For example:
“Delivery was faster than expected (positive), but the packaging was damaged (negative).”
With the help of AI, even complex, multi-faceted feedback like this can be accurately analyzed.
Moreover, AI identifies the specific topics that a review addresses. Topics such as customer service, product quality, delivery & shipping, price-performance ratio, and usability are often evaluated in customer comments.
Ultimately, this allows brands to not only analyze customer feedback as a whole but also to specifically evaluate individual topic areas. This enables companies to make improvements in these specific areas to demonstrate to customers that their opinions matter.
Sentiment analysis is an essential business intelligence tool in marketing that helps companies improve their brand image through the evaluation of customer comments.
Customer satisfaction is often measured through standardized surveys and key performance indicators such as the Net Promoter Score (NPS).
Common questions in online shopping are: “How satisfied were you with the purchase process?” or “Would you recommend our brand to a friend or colleague?”
After the buyer has given a rating from 0–10, they can leave a comment.
The average rating provides an indicator of how satisfied customers generally are. This is called the Net Promoter Score.
The goal of every company is to increase customer satisfaction.
The Net Promoter Score (NPS) measures the willingness of customers to recommend a product to friends or colleagues.
The rating given on a scale from 0 to 10 is classified into one of the three NPS categories: Promoters, Passives, or Detractors.
Promoters are customers who gave a rating of 9 or 10. They are extremely satisfied and gladly recommend the product to family, friends, and acquaintances without hesitation.
Passives give the product a rating of 7 or 8 and are only moderately satisfied with their purchase. It is possible that they still look at competitors and, if convinced, switch to them and do not remain loyal to the brand.
Detractors give a rating between 0 and 6 in the survey and represent consistently dissatisfied customers. These can influence the popularity and reputation of the brand through negative comments. Nevertheless, the comments of dissatisfied customers are valuable, because through them, brands can find out exactly what needs to be improved.
So far, so good. In the next step, after the rating, the customer can now leave a comment. That is the real gold!
Every brand naturally has its own channels that are easily accessible—for example, the online shop, emails, or even offline in physical stores. Here, the company can easily integrate customer surveys at all points of the customer journey. In-store, customers can be directed to an online survey via flyers with QR codes.
In addition, companies also offer their products on third-party platforms such as Amazon or Zalando, where customer feedback is found in the reviews of the respective products.
Customers also often have the opportunity to leave a review on comparison platforms. Well-known sites include Trustpilot, Google Reviews, Yelp, Facebook Reviews, Idealo Ratings, and Check24 Ratings.
How can a holistic view of customer feedback be created?
To create a comprehensive picture of customer feedback, a single platform that consolidates all the data and feedback from various channels, analyzes it, and ultimately derives improvements from it would be a good solution to this problem.
Platforms like zenloop make this process possible.
Currently, a large number of customer comments are spread across different channels. This often leads to a lack of a clear overall view of customer emotions.
With this single-platform approach, this problem can be resolved.
The following illustration places customer feedback at the center. Processing this feedback allows different departments—whether customer service, sales, or logistics—to be informed and make targeted improvements based on it.
Ultimately, it can be said that sentiment analysis is important for companies to quickly and easily draw conclusions about how customers feel and to identify the types of emotions expressed, such as complaints or joy.
Sentiment analyses play a central role especially in marketing analyses and the optimization of customer experiences. Due to digitalization, thousands of new reviews, customer comments, and feedback from social media and online shops flow in every day.
But where exactly does sentiment analysis provide real added value and save valuable time?
Strengthening brand awareness
Brand awareness plays a crucial role in business success. Sentiment analysis captures customer reviews and shows how the brand is perceived by the public. Essentially, every single positive comment about a brand and its products is free advertising. This process not only strengthens brand awareness but also promotes brand image and customer loyalty.
Detecting dissatisfied customers early
Complaints from dissatisfied customers are naturally unwelcome, but they leave valuable clues about where problems and optimization potentials exist. Sentiment analyses help to identify these critical customer voices and initiate targeted actions using the closing-the-loop approach. Thus, it can be said that it is very important and helpful for a company when customers express their dissatisfaction in comments.
Understanding sentiment trends in target groups
Depending on the region or target group, opinions about a product may vary. With the help of sentiment analysis, one can identify in which regions similar sentiments are found and how they change over time. This helps companies to adapt certain products to the individual needs of customers.
Sentiment analysis works by using technologies from artificial intelligence (AI). It involves natural language processing (NLP). Such tools take in written information like customer reviews and comments and identify whether the emotional expression conveys positivity, negativity, or neutrality. There are different approaches to interpreting the text’s correct sentiment:
What describes the rule-based sentiment analysis approach?
In rule-based sentiment analysis, predefined dictionaries are used, in which individual words are assigned to one of the three sentiment types – positivity, negativity, and neutrality – so that the overall sentiment in the text can be determined.
Examples: “great”=0.9, “bad”=-0.7, “okay”=0.1
What does the machine learning approach describe?
In the machine learning approach, computer software is trained by feeding it with already classified datasets. In these datasets, it is already known whether the emotional tone is positive, negative, or neutral.
Through repeated training with such examples, the computer model learns to detect the emotional sentiment in new, unknown datasets with high precision.
There is also the option to conduct a sentiment analysis manually. This method is well-suited when only a limited amount of customer data is available—often just a handful of data points. However, once more feedback is collected, the manual effort quickly becomes very time-consuming and impractical.
How does a manual sentiment analysis work?
Download a template – Use an Excel or Google Sheets template as your base.
Choose feedback channels – Typical sources include support conversations, customer reviews, or NPS/CSAT surveys.
Collect feedback – Gather and structure all feedback centrally in one tool (e.g., Google Sheets, Excel table).
In the template, you’ll find sample categories: Feedback, Channel, Positive/Negative Topics, Responsible Department, Sentiment.
Tag feedback – Categorize comments by topic and sentiment (e.g., positive, neutral, negative)—optionally using a scale. The feedback is also assigned a numerical sentiment score (1–5):
1 = very positive (“Happy”)
5 = very negative (“Angry”)
Report results – Visualize the analysis and derive clear action steps. For example, a simple, easy-to-read chart enables every team member to draw specific measures from it.
Through repeated training with such examples, the computer model learns to capture emotional sentiment with high precision in new, unknown data sets.
1. Know your starting point
Only those who understand how satisfied their customers currently are can make targeted improvements. A solid analysis provides the foundation to understand sentiment, identify trends, and take early corrective action.
2. Empower your employees
Give your service team more decision-making freedom—such as for small goodwill gestures. This saves time, avoids frustration, and shows customers that their concerns are taken seriously.
3. Personalize interactions
Personalized communication and relevant recommendations create a sense of connection. Customers who feel seen as individuals—not just as ticket numbers—are more likely to stay loyal and share positive feedback.
In conclusion, it is worthwhile for companies to invest in sentiment analysis. It enables the targeted collection of customer feedback and provides valuable insights to enhance the customer experience through customer comments. Overall, successful sentiment analysis helps strengthen relationships with customers and address their needs.
There are three main types:
NLP (Natural Language Processing) is the broader field that enables machines to understand human language. Sentiment analysis is a specific application of NLP focused on identifying the emotional tone (positive, neutral, negative) within texts.
Emotion analysis goes deeper than traditional sentiment analysis. It identifies specific emotions such as joy, anger, fear, or surprise — not just the overall sentiment.
Yes, ChatGPT can perform basic sentiment analysis, such as assessing individual statements. However, for large-scale, automated analysis, a specialized solution like zenloop is recommended.
Typically, numerical scores are used — for example, from -1 (negative) to +1 (positive). These values are generated by evaluating keywords, sentence structure, and context.
First, define your goal (e.g., improving customer satisfaction), select relevant data sources (e.g., customer feedback, social media), and use a suitable analysis tool — such as zenloop.
