For humans, the way we understand what’s being said is almost an unconscious process. To understand the meaning of text, 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. The semantic analysis of natural language content starts by reading all the words in the content to capture the real meaning of any text. It identifies the text elements and assigns them to their logical and grammatical role. It analyses context in the surrounding text and it scrutinises the text structure to remove uncertainty around the meaning of words that have more than one definition.
Well-trained text analysis tools can automatically analyse data in a matter of minutes, saving time and money, and providing valuable business insights. There are basic and more advanced text analysis techniques, each used for different purposes.
Emotions are essential to effective communication between humans, so if we want machines to handle texts in the same way, we need to teach them how to detect emotions and classify text as positive, negative or neutral. That’s where sentiment analysis comes into play. It’s the automated process of understanding an opinion about a given subject from written or spoken language.
With semantic-based data modelling in a smart data lake, all your data can be neatly organised using business models that the user defines, based on human-readable, standardised terms that allow you to link and contextualise information regardless of where it came from. And all this smart data can then be used to automatically create data extracts, ETL, and ELT jobs for quick and efficient analysis.
Because the data model has been created with a semantic approach, that model can be queried endlessly. Analysts can ask the model where data came from, what it means, and what conservation happened to that data. Bringing the data together from various sources, combining it together in a database using a customised domain model, and then conducting analytics on that combined data set creates a huge benefit and freedom to analysts, and to the organisation.