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Semantic Analysis: Definition and Use Cases in Natural Language Processing

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Semantic Analysis: Definition and Use Cases in Natural Language Processing

Artificial Intelligence tools are becoming more and more effective at processing natural language, thanks in particular to more sophisticated use of semantic analysis. In this article, we take a look at what semantic analysis is and how it can benefit organisations.

What is semantic analysis?

Definition of semantic analysis

Semantics refers to the study of words in context. One word can have several meanings. To understand its real meaning within a sentence, we need to study all the words that surround it. This is the context.

This makes it easier to understand words, expressions, sentences or even long texts (1000, 2000, 5000 words…).

As well as giving meaning to textual data, semantic analysis tools can also interpret tone, feeling, emotion, turn of phrase, etc. This analysis will then reveal whether the text has a positive, negative or neutral connotation.

Referred to as the world of data, the aim of semantic analysis is to help machines understand the real meaning of a series of words based on context. Machine Learning algorithms and NLP (Natural Language Processing) technologies study textual data to better understand human language. In this way, semantic analysis makes it possible to refine natural language processing.

Example of semantic analysis

To help you better understand semantic analysis, here are two examples:

Example 1:

  • I’m eating a strawberry ice cream.
  • I look at my reflection in the mirror on my wardrobe.

The word “ice” can have several meanings: a food or a mirror. To determine its real meaning in each sentence, we need to analyse the other components of the sentence. This is all the more important in French (and in European languages in general), since many words are particularly ambiguous. For these words, only the context allows us to understand their meaning.

Example 2:

  • Let’s eat, children!
  • Let’s eat children!

In addition to polysemous words, punctuation also plays a major role in semantic analysis.

In this example, the meaning of the sentence is very easy to understand when spoken, thanks to the intonation of the voice. But when reading, machines can misinterpret the meaning of a sentence because of a misplaced comma or full stop.

Why use semantic analysis?


Semantic analysis applies very well to natural referencing. It involves helping search engines to understand the meaning of a text in order to position it in their results. Google will then analyse the vocabulary, punctuation, sentence structure, words that occur regularly, etc.

As SEO has evolved, the study of semantic analysis has become more refined. Originally, natural referencing was based essentially on the repetition of a keyword within a text. But as online content multiplies, this repetition generates extremely heavy texts that are not very pleasant to read.

To improve the user experience, search engines have developed their semantic analysis. The idea is to understand a text not just through the redundancy of key queries, but rather through the richness of the semantic field.

To take the example of ice cream (in the sense of food), this involves inserting words such as flavour, strawberry, chocolate, vanilla, cone, jar, summer, freshness, etc.

As far as Google is concerned, semantic analysis enables us to determine whether or not a text meets users’ search intentions.


In addition to natural search, semantic analysis is used for chatbots, virtual assistants and other artificial intelligence tools.

As well as having to understand the user’s intention, these technologies also have to render content on their own. But if the Internet user asks a question with a poor vocabulary, the machine may have difficulty answering.

Marketing and social listening

As we saw earlier, semantic analysis is capable of determining the positive, negative or neutral connotation of a text. This skill is particularly useful in the field of marketing. Machines can automatically understand customer feedback from social networks, online review sites, forums and so on. More specifically, they need to transcribe customer emotions. In other words, they need to detect the elements that denote dissatisfaction, discontent or impatience on the part of the target audience.

By using semantic analysis tools, brands are able to process large volumes of textual data. In so doing, they develop their customer knowledge and their understanding of market trends. As a result, they can improve their communications, and set up alerts if too many negative messages appear…

Ultimately, semantic analysis is an excellent way of guiding marketing actions.

What other types of textual analysis are there?

Syntactic analysis

Syntactic analysis involves analysing the structure of the sentence itself.

For example:

Are you eating an ice cream?
You are eating an ice cream

In this context, the subject-verb positioning makes it possible to differentiate these two sentences as a question and a statement. However, the meaning of the words as such is not analysed. This is only done a posteriori with the semantic analysis.

This is why syntactic and semantic analyses are complementary.

Lexical analysis

Lexical analysis is particularly used in programming languages. Here, the aim is to study the structure of a text, which is then broken down into several words or expressions. For example, identifiers, keywords, separators, etc.

Here again, semantic analysis is performed after the fact.

Key facts:

  • Semantic analysis makes it possible to understand the meaning of a word, a sentence, an expression or a text, thanks to the context provided by all the textual data.
  • This analysis is particularly useful for natural language processing, as it enables machines to better understand the human brain.
  • In practical terms, this study of semantics has applications in marketing, chatbots and SEO.

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