What Is Semantic Analysis?

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Semantic analysis helps natural language processing (NLP) figure out the correct concept for words and phrases that can have more than one meaning.

[Featured Image] An office worker uses semantic analysis to improve her company's customer service.

Semantic analysis allows computers to interpret the correct context of words or phrases with multiple meanings, which is vital for the accuracy of text-based NLP applications. Essentially, rather than simply analyzing data, this technology goes a step further and identifies the relationships between bits of data. Because of this ability, semantic analysis can help you to make sense of vast amounts of information and apply it in the real world, making your business decisions more effective.

Semantic analysis offers your business many benefits when it comes to utilizing artificial intelligence (AI). Semantic analysis aims to offer the best digital experience possible when interacting with technology as if it were human. This includes organizing information and eliminating repetitive information, which provides you and your business with more time to form new ideas.   

What is semantic analysis?

This procedure allows machines to decipher the human intent behind words and sentences, making it a key component of 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 more on how pieces of data work together instead of just focusing solely on the data as singular words strung together. Understanding the human context of words, phrases, and sentences gives your company the ability to build its database, 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. 

How semantic analysis works

Because machines don’t have the ability to determine words and phrases like 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, 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 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. 

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 an average annual salary of $122,734, according to January 2024 data from Glassdoor [1]. Additionally, the US Bureau of Labor Statistics estimates that the field in which this profession resides is predicted to grow 35 percent from 2022 to 2032, indicating above-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 really 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.

Getting started with Coursera

Learn more about how semantic analysis can help you further your computer NSL knowledge. Check out the Natural Language Processing and Capstone Assignment from the University of California, Irvine. Or, delve deeper into the subject by complexing the Natural Language Processing Specialization from DeepLearning.AI—both available on Coursera. 

Article sources


Glassdoor. “NLP Engineer Salaries, https://www.glassdoor.com/Salaries/nlp-engineer-salary-SRCH_KO0,12.htm.” Accessed March 20, 2024.

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