What Is Semantic Analysis?

Key takeaways

Semantic analysis helps machines understand the human intent behind words and sentences, making it a key component of natural language processing.

• Semantic analysis, when combined with lexical, grammatical, and syntactical analysis, enables machines to understand words and phrases more closely to the ways that humans do.

• Semantic analysis can help you quickly and efficiently gain insights and feedback from your data. 

• You can use your knowledge of semantic analysis in a role as a natural language processing engineer. 

Discover details about semantic analysis and learn more about how it works, the pros and cons associated with the process, and how you can start a career in a field that relies on semantic analysis. If you’re ready to learn more cutting-edge NLP techniques, enroll in the Natural Language Processing Specialization. You’ll have the opportunity to gain experience in text mining, feature engineering, artificial neural networks, data processing, and much more. 

What is semantic analysis?

This procedure allows machines to decipher the human intent behind words and sentences, making it a key component of natural language processing (NLP), which allows a computer to comprehend language. This enables computers to learn nuances and meanings that happen during human communication. Additionally, although semantic analysis is a process that contributes greatly to NLP within various machine learning tools such as chatbots, text analysis, and search engines, this technology can also assist your company in gleaning important information from seemingly random data within emails, customer feedback, and support tickets. 

What sets semantic analysis apart from other technologies is that it focuses on how pieces of text work together rather than viewing them as individual words strung together. Machine learning models that understand the human context of words, phrases, and sentences give your company the ability to build stronger databases, allowing you to access more information and make informed decisions. Two useful methods in semantic analysis include the following.

Combining machine learning with natural language processing 

Using machine learning with natural language processing enhances a machine's ability to decipher what the text is trying to convey. This semantic analysis method usually takes advantage of machine learning models to help with the analysis. For example, once a machine learning model has been trained on a massive amount of information, it can use that knowledge to examine a new piece of written work and identify critical ideas and connections.

Creating a database of information broken down into specific subjects

If you use a text database about a particular subject that already contains established concepts and relationships, the semantic analysis algorithm can locate the related themes and ideas, understanding them in a fashion similar to that of a human. This method makes it quicker to find pertinent information among all the data. 

What is semantic analysis with an example?

Since semantic analysis works to understand the meaning behind a sentence, it can help to use an example to illustrate its importance in machine learning. Take, for example, the spoken sentence:

“That apple is so sweet!”

This phrase could refer to the fruit, an apple, or the brand Apple. To know for certain what the sentence is referring to, you would need the context surrounding it. Semantic analysis is the process of analyzing this context and parsing out the meaning of each word. 

How does semantic analysis work?

Because machines don’t have the ability to determine words and phrases as humans do, they rely on semantic analysis. It consists of four parts that work together:

• Lexical analysis: This plays a major role in semantic analysis because it changes a stream of characters into information that a computer understands.

• Grammatical analysis: This process identifies the order of words and then tags each word as a particular part of speech—noun, verb, or adjective. Doing so creates a grammatical structure, bringing the machine closer to comprehension.

• Syntactical analysis: This type of analysis breaks down the syntax and then uses grammatical rules to give meaning to the words and sentences.

• Semantic analysis: Finally, this analysis combines all of the aforementioned processes to discover meanings in words and sentences, which helps machines understand and use language the way humans do.

Semantic analysis vs. sentiment analysis

Both semantic and sentiment analysis are valuable techniques used for NLP, a technology within the field of artificial intelligence (AI) that allows computers to interpret and understand words and phrases like humans. Semantic analysis uses the context of the text to attribute the correct meaning to a word with several meanings. On the other hand, sentiment analysis determines the subjective qualities of the text, such as feelings of positivity, negativity, or indifference. This information can help your business learn more about customers’ feedback and emotional experiences, which can assist you in making improvements to your product or service. 

Pros and cons of semantic analysis

Semantic analysis helps many businesses grow and improves customer service, which has many benefits. You can use it to gain insights and feedback from customer reviews and allow quicker responses to emails, messages, and chatbots. For example, Uber’s use of semantic analysis has proven successful for the company when developing a new app version. Because of semantic analysis, the company can efficiently track users' comments, thoughts, and issues on social networks. Reviewing social networks for information about your product or service is called “social listening,” which can help you improve your business. 

By organizing myriad data, semantic analysis in AI can help find relevant materials quickly for your employees, clients, or consumers, saving time in organizing and locating information and allowing your employees to put more effort into other important projects. Your company can also review and respond to customer feedback faster than manually. This analysis is key when it comes to efficiently finding information and quickly delivering data. It is also a useful tool to help with automated programs, like when you’re having a question-and-answer session with a chatbot. 

One limitation of semantic analysis occurs when using a specific technique called explicit semantic analysis (ESA). ESA examines separate sets of documents and then attempts to extract meaning from the text based on the connections and similarities between the documents. The problem with ESA occurs if the documents submitted for analysis do not contain high-quality, structured information. Additionally, if the established parameters for analyzing the documents are unsuitable for the data, the results can be unreliable.

Who uses semantic analysis?

The amount and types of information can make it difficult for your company to obtain the knowledge you need to help the business run efficiently, so it is important to know how to use semantic analysis and why. Using semantic analysis to acquire structured information can help you shape your business's future, especially in customer service. In this field, semantic analysis allows options for faster responses, leading to faster resolutions for problems. Additionally, for employees working in your operational risk management division, semantic analysis technology can quickly and completely provide the information necessary to give you insight into the risk assessment process. 

How to start a career related to semantic analysis

If you’re interested in a career that involves semantic analysis, working as a natural language processing engineer is a good choice. Essentially, in this position, you would translate human language into a format a machine can understand. Depending on the industry in which you work, your responsibilities could include designing NLP systems, defining data sets for language learning, identifying the proper algorithm for NLP projects, and even collaborating with others to convey technical information to people without your background. 

If you decide to work as a natural language processing engineer, you can expect to earn a median total salary of $161,000 according to December 2025 data from Glassdoor [1]. This figure includes both base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other forms of compensation. Additionally, the US Bureau of Labor Statistics estimates that the field in which this profession resides is predicted to grow 34 percent from 2024 to 2034, indicating much faster-than-average growth and a positive job outlook [2].

To become an NLP engineer, you’ll need a four-year degree in a subject related to this field, such as computer science, data science, or engineering. If you want to increase your employability, earning a master’s degree can help you acquire a job in this industry. Finally, some companies provide apprenticeships and internships in which you can discover whether becoming an NLP engineer is the right career for you.

Read more: How to Land a Machine Learning Internship

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