![]() To feed marketers demand for sentiment, social analytics platforms began offering “hot or cold” analyses of topics and brands.īut what is sentiment analysis? In truth, it’s nothing more than an application of explicit understanding (e.g. Rather, it is to say you must use tags in accordance with an authoritative, data-driven taxonomy or a defined set of rules.) Sentiment analysis makes a brief splashĪs social media and user-generated content started taking over the internet in the early 2000s, marketers began developing an insatiable appetite for meaning in data-not just knowing whether individual consumers were talking about a given brand, but how those consumers felt towards the brand, its products, and its services. (To be clear, this isn’t to say marketers should stop tagging their content-tags are an important component of semantic understanding. Ultimately, tagging proved to be no better than an educated guess of end-user intention. But like textual analysis, tagging came with a laundry list of limitations-redundant tags, misspelled tags, inconsistently applied tags, over-tagging, etc. Tagging attempted to use human understanding of content to create keyword-based guidelines machines could follow to identify important content (content relevant to an individual searcher’s underlying need). However, a textual analysis for the word “whip” might just as easily capture a result like this…Īnnnnnd that would be a problem, wouldn’t it? Then there was tagging… …which is great because we were thinking of the word “whip” in the context of a “bullwhip.” Yet this belief made many erroneous assumptions, chief among them being that “context doesn’t matter.”īut as anyone who’s ever been on a date, gone to a comedy show, or had the most basic of human interactions can tell you, “context matters a helluva lot.” Without the surrounding context, the frequency of a word is pretty meaningless.Ī textual analysis for the word “whip” might capture a result like this… Why? Because there was a belief that word frequency was tied to importance-the more important the word, the more frequently it will appear. ![]() In the early days of MarTech, people wrote programs to scrape huge amounts of data for recurring words and phrases (remember word clouds?). Extract relevant and useful information from large bodies of unstructured dataĪnd so much more! Before semantic analysis, there was textual analysis.Find an answer to a question without having to ask a human.Discover the meaning of colloquial speech in online posts.Advancing algorithms, increasingly powerful computers, and data-based practice have made machine-driven semantic analysis a real thing with a number of real world applications. They couldn’t process context to understand what material is relevant to predicting an outcome and why.īut the evolution of Artificial Intelligence, machine learning, and natural language processing has changed all that. Machines couldn’t do what we doįor the bulk of recorded history, semantic analysis was the exclusive competence of man-tools, technologies, and machines couldn’t do what we do. …and then use the output of that analysis to predict an outcome with incredible accuracy. ![]() Compare that information against prior experience (or a taxonomy in the case of machines).Context surrounding words, phrases, objects, scenarios, etc.In fact, we’re so good at it’s generally an unconscious exercise, like breathing…we just do it without thinking about it. Humans do semantic analysis incredibly well. ![]() What you just did right there?-That’s semantic analysis (SA). You used these two contextual clues to understand the implied meaning behind the title What Is Semantic Analysis? and accurately predict what this blog is going to discuss. The publication (you know Zeta blog publishes content targeted at marketers).The title (it literally says “what is semantic analysis”).This blog is going to answer the questions of ‘what is semantic analysis’ and ‘why semantics is important’ to marketers …but we didn’t have to tell you that, did we?
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