applied NLP simple text analysis and visualisation of semantic entities

brief and shallow description of the application:
1. solution: generate a word cloud, in order to visualise the distribution frequencies of semantic entities in a structured discourse (or within a predefined representation system).
2. approach and methods:
– define discourse structure or domain of analysis: wiki article for the search term – NLP (or natural language processing)
– extract natural language content or raw text data from the article by applying various methods for boilerplate removal (like a stopword filter for example)
– convert extracted text to (a standardised raw coding format that is readable for or recognised by machines on a binary level) unicode utf-8
– apply several processes (i.e methods or programs developed in the fields of computational linguistics and corpus-linguistics) to the converted text corpus
– for the current case: a simple text analysis and visualisation of semantic entities, the text corpus of the converted wiki article for nlp has been lemmatized
– In a very lose sense, lemmatization can be regarded as a semantic word analysis that counts and measures the most basic or core forms for every word that occurs in the corpus and their frequencies. Furthermore semantic relationships between the words forms.
– based on the given descriptive context, the lemmatized state of the text corpus allows a visualisation that shat shows the analysed basic semantic word forms, their distribution frequencies and some other specific aspects by translating the expressed relationships of natural language data into an optical framework that enables the dynamic development of intellectual comprehension, whatsoever – just see the iframe if its functional
Voyant Tools Visualiation