Natural Language Process semantic analysis: definition
Semantic analysis: When you really want to understand meaning in text
Social media, blog posts, comments in forums, documents, group chat applications or dialog with customer service chatbots: Text is at the heart of how we communicate with companies online.
Each type of communication, whether it’s a tweet, a post on LinkedIn or a review in the comments section of a website, contains potentially relevant, even valuable information that must be captured and understood by companies who want to stay ahead. Capturing the information isn’t the hard part. What’s really difficult is understanding what is being said, and doing it at scale.
For humans, the way we understand what’s being said is almost an unconscious process. To understand what a text is talking about, we rely on what we already know about language itself and about the concepts present in a text. Machines can’t rely on these same techniques.
Some technologies only make you think they understand text. An approach based on keywords or statistics, or even pure machine learning, may be using a matching or frequency technique for clues as to what a text is “about.” These methods can only go so far because they are not looking at meaning.
Semantic analysis describes the process of understanding natural language–the way that humans communicate–based on meaning and context. Let’s look at how a cognitive technology like Cogito performs semantic analysis.
The semantic analysis of natural language content starts by reading all of the words in content to capture the real meaning of any text. It identifies the text elements and assigns them to their logical and grammatical role. It analyzes context in the surrounding text and it analyzes the text structure to accurately disambiguate the proper meaning of words that have more than one definition.
Semantic technology processes the logical structure of sentences to identify the most relevant elements in text and understand the topic discussed. It also understands the relationships between different concepts in the text. For example, it understands that a text is about “politics” and “economics” even if it doesn’t contain the the actual words but related concepts such as “election,” “Democrat,” “speaker of the house,” or “budget,” “tax” or “inflation.”
Because semantic analysis and natural language processing can help machines automatically understand text, this supports the even larger goal of translating information–that potentially valuable piece of customer feedback or insight in a tweet or in a customer service log–into the realm of business intelligence for customer support, corporate intelligence or knowledge management.
Originally published November 2017, updated March 2020