At a time when 87% of French people read customer reviews before making a purchasing decision 1 , it is essential for companies to know how to study and manage them.
But managing customer reviews , whether for an establishment or a network, can be time-consuming given the large volume of data to be processed.
Semantic analysis helps meet this need by processing, analyzing and kenya mobile phone number list categorizing your customer reviews, regardless of their volume. But what is actually behind semantic analysis?
1. Semantic analysis: definition
2. Use semantic analysis for customer reviews
Conclusion
definition and issues semantic analysis
1. Semantic analysis: definition
Semantic analysis is the study of words in their context 2 .
This analysis focuses on two axes:
The form of the message : the relationship between the different words that compose it
The heart of the message : the meaning of the words when they are together
For example, consider these two customer reviews on a clothing store’s Google My Business listing.
The first one says, “ I tried to reach the top shelf, but it wasn’t accessible .” In this review, the customer uses the word “accessible” to refer to the store’s layout.
The second customer writes, “ The products in this store are really accessible. I was able to buy several items without breaking my budget .” The same word is used, but in this context, it refers to the price of the products.
A semantic analysis program will be able to correctly analyze the meaning of the word “accessible” in these two sentences, by deciphering the associated context.
use semantic analysis for customer reviews
2. Use semantic analysis for customer reviews
Semantic analysis also allows us to identify emotions , whether positive, neutral or negative, making it an ideal tool for processing and analyzing customer reviews .
Identify clear emotions
During an in-store experience, the consumer will experience different stages and feel different feelings . Some will be good, others less so or even bad.
For example, when you go to a coffee shop, you notice that there are a lot of people. You don't want to go anywhere else, so you queue up. The waiting time is very long: 20 minutes for your hot chocolate. However, the employees served you with a big smile and apologized for the wait. In addition, your drink was very good.
During your purchasing journey, you therefore felt several emotions:
Frustration due to waiting: negative emotion
Staff friendliness: positive emotion
Drink quality: positive emotion
If you write a customer review after this experience as in the example below, you will include both positive and negative . Semantic analysis will be able to distinguish between positive and negative emotions and categorize them .
customer reviews emotions identified with semantic analysis
Example of a customer review incorporating both positive and negative emotions
Beyond the binarity of a simple keyword analysis, it therefore creates nuance in order to most faithfully translate the feelings felt by customers.
How to do a semantic analysis on customer reviews?
Semantic analysis can be implemented using a Deep Learning algorithm : it is an Artificial Intelligence that learns as it is used, meaning that the more data it has to process, the more it improves and optimizes its analyses and conclusions.
In the case of customer reviews, the more she studies them, the more precisely she will be able to detect the emotions, tone and substance of the message left by your customers.
There are two types of Deep Learning:
Supervised : as its name suggests, it is set up and assisted by a human who programs it a task. For example, it can be told to analyze all the opinions concerning the quality of the product sold and to associate a positive or neutral emotion with them.
Unsupervised : Conversely, it is not programmed and does not rely on external elements. Its learning is autonomous.
Digitaleo has just launched the semantic analysis by AI of customer reviews on October 3rd . In order to improve monitoring, every quarter, a complete report analyzing 16 different themes (from atmosphere to product to cleanliness) is sent to our customers.
In the example below, the customer left a very positive review. Semantic analysis allows us to convey both their overall good feeling and the details of what they appreciated. Each element is processed to be categorized.
customer reviews semantic analysis
Example of a customer review analyzed with Digitaleo semantic analysis AI
Conclusion
Analyzing customer reviews is a key success factor for any business because it allows you to identify many areas for improvement. Unfortunately, it takes a lot of time and the amount of information can make the task complex. Action plans in turn become complicated to implement.
Semantic analysis helps you in this process and saves you a lot of time by automatically processing, analyzing and categorizing all your customer reviews. It allows you to separate your strengths and weaknesses , according to different categories studied. The implementation of corrective actions will be all the more effective and easy.
Semantic analysis of customer reviews: issues and definition
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