What Are Some Examples of Semantic Analysis?

Semantic analysis in Natural Language Processing (NLP) interprets the meaning and context behind human language, moving beyond simple keyword matching to understand intent, relationships, and context. Common examples include Sentiment Analysis, Word Sense Disambiguation, Named Entity Recognition, Intent Detection, and Semantic Role Labelling.  

These techniques allow AI systems to analyse tone, identify entities, resolve ambiguity, and interpret user goals accurately. 

In this blog, you will explore what are some examples of semantic analysis, see how they work in real applications.  

If you want to go beyond the basics of NLP and build real expertise, explore upGrad’s Artificial Intelligence courses and gain hands-on skills from experts today!  

Examples of Semantic Analysis in NLP 

If you are asking what are some examples of semantic analysis, here are the most common techniques used in AI systems today: 

• Sentiment Analysis 
• Word Sense Disambiguation 
• Named Entity Recognition 
• Intent Detection 
• Semantic Role Labelling 

These examples show how machines interpret meaning at different levels, from individual words to full conversations. Each one solves a specific language understanding challenge. 

Here is a quick summary: 

Example	What It Does	Used In
Sentiment Analysis	Detects emotional tone	Reviews, social media
Word Sense Disambiguation	Identifies correct word meaning	Search engines
Named Entity Recognition	Detects names and places	Information extraction
Intent Detection	Understands user purpose	Chatbots
Semantic Role Labeling	Identifies who did what	Language understanding systems

Together, these techniques help AI systems move beyond simple text matching and understand real meaning. 

Also Read: How Does NLP Work Step by Step in AI?  

Now let’s look at each example clearly. 

1. Sentiment Analysis 

Sentiment analysis is one of the most widely used techniques when discussing what are some examples of semantic analysis. It focuses on identifying the emotional tone behind a piece of text. 

It helps answer: 

• Is the text positive, negative, or neutral? 
• What emotion does the sentence express? 

Example: 

“The product quality is excellent.” 

The system detects positive sentiment based on the word “excellent” and overall tone. 

Businesses use sentiment analysis to monitor customer reviews, track brand perception, and understand public opinion on products or services. 

Also Read: Which NLP Model Is Best for Sentiment Analysis in 2026?  

2. Word Sense Disambiguation 

Word Sense Disambiguation is another important technique when exploring what are some examples of semantic analysis. It focuses on identifying the correct meaning of a word that has multiple interpretations. 

It helps answer: 

• Which meaning of a word fits this context? 
• Is the word being used literally or in a specific domain sense? 

Example: 

“I deposited money in the bank.” 
“I sat by the bank of the river.” 

The word “bank” has different meanings in each sentence. The system uses surrounding words to determine the correct interpretation. 

Search engines and language models rely on this method to avoid misunderstandings and deliver accurate results. 

Also Read: 35+ Essential NLP Interview Questions and Answers to Excel

3. Named Entity Recognition 

Named Entity Recognition is another key method when explaining what are some examples of semantic analysis. It focuses on identifying and classifying important entities in text. 

It helps answer: 

• Is this word a person, place, or organization? 
• Is this a date, product, or event? 

Example: 

“Microsoft announced a new product in India.” 

The system identifies “Microsoft” as an organization and “India” as a location. 

NER is widely used in search engines, information extraction, news analysis, and recommendation systems to structure unorganized text data. 

Also Read: 10+ NLP Tools You Should Know in 2026 

4. Intent Detection 

Intent Detection is another strong example when discussing what are some examples of semantic analysis. It focuses on understanding the goal behind a user’s message. 

It helps answer: 

• What does the user want to do? 
• Is the user asking, booking, complaining, or requesting support? 

Example: 

“Cancel my subscription.” 

The system identifies the intent as cancellation. 

Chatbots, virtual assistants, and customer support systems rely on intent detection to provide accurate and relevant responses based on user goals. 

Also Read: Top 10 Natural Language Processing Examples in Real Life 

5. Semantic Role Labelling 

Semantic Role Labelling is another important technique when exploring what are some examples of semantic analysis. It focuses on identifying the role each word plays in a sentence. 

It helps answer: 

• Who performed the action? 
• What action was taken? 
• Who or what was affected? 

Example: 

“Anita sent an email to Rahul.” 

The system identifies Anita as the sender, email as the object, and Rahul as the recipient. 

This technique helps AI systems understand relationships within sentences and improves deeper language understanding. 

Also Read: Parsing in Natural Language Processing: A Complete Guide  

Conclusion 

Understanding what are some examples of semantic analysis helps you see how AI systems truly interpret language. From detecting sentiment to identifying intent and resolving word meaning, semantic analysis allows machines to move beyond keywords and grasp real context. These techniques power chatbots, search engines, and modern NLP applications across industries. 

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