The modern world is positively driven by technological advancement and rapid digitalisation, and our ever-present obsession with these modern marvels has resulted in technology revitalising practically every aspect of life to its modern iteration. When it comes to genuinely understanding data-driven technological innovations, it is fair to say that sometimes they can go over one’s head if they are not explained well enough. The modern world is positively driven by data, and so it pays to understand some of that data in more depth. Understanding and successfully transmitting technical SEO for content is one of the most profoundly important efforts in data transference and storage, and so this is one of the most important branches of data technology to understand.
Consider the Natural Language API by Google, for example. This is a decidedly intricate process to grasp, but once the basics are nailed down it becomes infinitely easier. Natural Language API works with multiple methods of performing analysis and annotations on given text chains. There are five primary levels, and each of them provides important information for language processing. One uses different levels to appropriate different responses and yield different results. Of all the technological feats of digitalisation that Google has successfully brought to the world, Natural Language APIs just might be one of the most important and unique of them all.
This Cloud Natural Language API successfully hands over natural language understanding tech to developers at the end of its inception. In addition to their specified approaches, ach API method can also track and return a language in the case that one is not specified by the called in the initial request to begin with. If one wishes to perform multiple natural language processes using a single API call, a special request (anotateText) is used to perform sentiment analysis and entity analysis.
Entity analysis works its magic by inspection the given text for known entities (think Proper nouns, Common nouns, etc). It then goes on to return information about those entities back to the system.
Entity sentiment analysis
Entity sentiment analysis, on the other hand, inspects the text for known entities (again, Proper nouns, Common nouns), then going on to return the information about those entities. It then identifies the overarching emotional arc of the entity in the given text. This is how it can figure out a writer’s attitude toward the entity as being either a) positive, b) negative, or c) neutral.
Sentiment analysis is interesting, because it sifts through the given information and identifies the most significant emotional response in the text. In some ways, sentiment analysis is a simplistic iteration of entity sentiment analysis.
Syntactic analysis, on the other hand, extracts the linguistic tones of the given text. It then goes on to break up said text into a series of sentences and word boundaries. The goal is to provide further analysis on said boundaries.
Simply put, content classification works to analyse written word, returning a content category for the analysed text. It really is that simple.