1/30/2024 0 Comments Textual data analysisSpanish? Singlish? Arabic? Each language has its own idiosyncrasies, so it’s important to know what we’re dealing with.Īs basic as it might seem, language identification determines the whole process for every other text analytics function. The first step in text analytics is identifying what language the text is written in. 1 – Lexalytics’ (an InMoment company) text analytics technology and NLP feature stack, showing the layers of processing each text document goes through to be transformed into structured data. Let’s review each step in order, and discuss the contributions of machine learning and rules-based NLP. There are 7 basic steps involved in preparing an unstructured text document for deeper analysis:Įach step is achieved on a spectrum between pure machine learning and pure software rules. Tearing apart unstructured text documents into their component parts is the first step in pretty much every NLP feature, including named entity recognition, theme extraction, and sentiment analysis. Much like a student writing an essay on Hamlet, a text analytics engine must break down sentences and phrases before it can actually analyze anything. Okay, now let’s get down and dirty with how text analytics really works. Text mining is how a business analyst turns 50,000 hotel guest reviews into specific recommendations how a workforce analyst improves productivity and reduces employee turnover how healthcare providers and biopharma researchers understand patient experiences and much, much more. Quick background: text analytics (also known as text mining) refers to a discipline of computer science that combines machine learning and natural language processing (NLP) to draw meaning from unstructured text documents. In this article I’ll review the basic functions of text analytics and explore how each contributes to deeper natural language processing features. But the core concepts are pretty easy to understand even if the actual technology is quite complicated. Text analytics and natural language processing (NLP) are often portrayed as ultra-complex computer science functions that can only be understood by trained data scientists.
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