Semantic Analysis: Working and Techniques

  • Bhumika Dutta
  • Jul 23, 2021
  • NLP
Semantic Analysis: Working and Techniques title banner

Semantic Analysis


Humans interact with each other through speech and text, and this is called Natural language. Computers understand the natural language of humans through Natural Language Processing (NLP). 


NLP is a process of manipulating the speech of text by humans through Artificial Intelligence so that computers can understand them. It has made interaction between humans and computers very easy.


(Recommended read : Top 10 Applications of NLP)


Human language has many meanings beyond the literal meaning of the words. There are many words that have different meanings, or any sentence can have different tones like emotional or sarcastic. It is very hard for computers to interpret the meaning of those sentences.


Semantic analysis is a subfield of NLP and Machine learning that helps in understanding the context of any text and understanding the emotions that might be depicted in the sentence. This helps in extracting important information from achieving human level accuracy from the computers. Semantic analysis is used in tools like machine translations, chatbots, search engines and text analytics


In this blog, you will learn about the working and techniques of Semantic Analysis.



How does Semantic Analysis work?


According to this source, Lexical analysis is an important part of semantic analysis. Lexical semantics is the study of the meaning of any word. In semantic analysis, the relation between lexical items are identified. Some of the relations are hyponyms, synonyms, Antonyms, Homonyms etc.


Let us learn in details about the relations:


  • Hyponymy: It illustrates the connection between a generic word and its occurrences. The generic term is known as hypernym, while the occurrences are known as hyponyms.

  • Homonymy: It may be described as words with the same spelling or form but diverse and unconnected meanings.

  • Polysemy: Polysemy is a term or phrase that has a different but comparable meaning. To put it another way, polysemy has the same spelling but various and related meanings.

  • Synonymy: It denotes the relationship between two lexical elements that have different forms but express the same or a similar meaning.

  • Antonymy: It is the relationship between two lexical items that include semantic components that are symmetric with respect to an axis. 

  • Meronomy: It is described as a logical arrangement of letters and words indicating a component portion of or member of anything.


 Through identifying these relations and taking into account different symbols and punctuations, the machine is able to identify the context of any sentence or paragraph. 


(Read also: What is text mining?)


Meaning Representation:


Semantic analysis represents the meaning of any sentence. These are done by different processes and methods. Let us discuss some building blocks of the semantic system:


  • Entities: Any sentence is made of different entities that are related to each other. It represents any individual category such as name, place, position, etc. We will discuss in detail about entities and their correlation later in this blog.

  • Concepts: It represents the general category of individual, such as person, city etc.

  • Relations:  It represents the relation between different entities and concepts in a sentence. 

  • Predicates: It represents the verb structure of any sentence.


There are different approaches to Meaning Representations according, some of them are mentioned below:


  • First-order predicate logic (FOPL)

  • Frames

  • Semantic Nets

  • Case Grammar

  • Rule-based architecture

  • Conceptual graphs 

  • Conceptual dependency (CD)


(Related blog: Sentiment Analysis of YouTube Comments)


Meaning Representation is very important in Semantic Analysis because:


  1. It helps in linking the linguistic elements of a sentence to the non-linguistic elements.

  2. It helps in representing unambiguous data at lexical level.

  3. It helps in reasoning and verifying correct data.


Processes of Semantic Analysis:


The following are some of the processes of Semantic Analysis:


  1. Word Sense disambiguation:


It is an automatic process of identifying the context of any word, in which it is used in the sentence. In natural language, one word can have many meanings. For eg- The word ‘light’ could be meant as not very dark or not very heavy. The computer has to understand the entire sentence and pick up the meaning that fits the best. This is done by word sense disambiguation.


  1. Relationship Extraction:


In a sentence, there are a few entities that are co-related to each other. Relationship extraction is the process of extracting the semantic relationship between these entities. In a sentence, “I am learning mathematics”, there are two entities, ‘I’ and ‘mathematics’ and the relation between them is understood by the word ‘learn’.


(Also read: NLP library with Python)


Techniques of Semantic Analysis:


There are two types of techniques in Semantic Analysis depending upon the type of information that you might want to extract from the given data. These are semantic classifiers and semantic extractors. Let us briefly discuss them.


  1. Semantic Classification models:


These are the text classification models that assign any predefined categories to the given text.


  • Topic classification:


It is a method for processing any text and sorting them according to different known predefined categories on the basis of its content. 


For eg: In any delivery company, the automated process can separate the customer service problems like ‘payment issues’ or ‘delivery problems’, with the help of machine learning. This will help the team notice the issues faster and solve them.


(Related read: Text cleaning and processing in NLP)


  • Sentiment analysis: 


It is a method for detecting the hidden sentiment inside a text, may it be positive, negative or neural. This method helps in understanding the urgency of any statement. In social media, often customers reveal their opinion about any concerned company. 


For example, someone might comment saying, “The customer service of this company is a joke!”. If the sentiment here is not properly analysed, the machine might consider the word “joke” as a positive word.


Latent Semantic Analysis: It is a method for extracting and expressing the contextual-usage meaning of words using statistical calculations on a huge corpus of text. LSA is an information retrieval approach that examines and finds patterns in unstructured text collections as well as their relationships.


  • Intent classification:


It is a method of differentiating any text on the basis of the intent of your customers. The customers might be interested or disinterested in your company or services. Knowing prior whether someone is interested or not helps in proactively reaching out to your real customer base.



  1. Semantic Extraction Models:


  • Keyword Extraction: 


It is a method of extracting the relevant words and expressions in any text to find out the granular insights. It is mostly used along with the different classification models. It is used to analyze different keywords in a corpus of text and detect which words are ‘negative’ and which words are ‘positive’. The topics or words mentioned the most could give insights of the intent of the text.


  • Entity extraction: 


As mentioned earlier in this blog, any sentence or phrase is made up of different entities like names of people, places, companies, positions, etc. This method is used to identify those entities and extract them. 


It can be very useful for customer service teams of businesses like delivery companies as the machine can automatically extract the names of their customers, their location, shipping numbers, contact information or any other relevant or important data.


(Recommended read: Word embedding in NLP using python code)





In any customer centric business, it is very important for the companies to learn about their customers and gather insights of the customer feedback, for improvement and providing better user experience. 


With the help of machine learning models and semantic analysis, machines can easily extract meaning from unstructured data gathered from their customer base in real time. It helps the company get accurate feedback that drives better decision-making and as a result improves the customer base.