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What is the Process of Text Mining?
Text mining is also known as text analysis. It is the process of transforming unstructured text into structured data for easy analysis. Text mining needs natural language processing (NLP), enabling devices to learn the human language and process it automatically.
It is defined as the process of extracting essential data from standard language text. Some data that we generate via text messages, documents, emails, files are written in common language text. Text mining is generally used to draw beneficial insights or patterns from such data.
Text mining is an automatic procedure that uses natural language processing to derive valuable vision from unstructured text. It can be transforming data into information that devices can learn, text mining automates the process of classifying texts by sentiment, subject, and intent.
The text mining process contains the following steps to extract the data from the files which are as follows −
Document Gathering − In the first step, the text documents are collected, which are present in several formats. The document can be in form of pdf, word, html doc, css, etc.
Document Pre-Processing − In this process, the given input document is processed for eliminating redundancies, inconsistencies, independent words, stemming and files are prepared for the next step, and the stages implemented are as follows −
Tokenization − The given document is treated as a string and recognized single word in the document i.e. the given document string is split into one unit or token.
Removal of Stop word − In this process the removal of constant words such as a, an, but, and, of, the, etc.
Stemming − A stem is a natural set of words with similar meanings. This approach defines the base of a specific word. There are two types of methods are Inflectional and derivational stemming. One of the famous algorithms for stemming is porter’s algorithm such as if a document pertains to words like resignation, resigned, resigns then it will be treated as resigning after using the stemming method.
Text Transformation − A text document is a set of words (feature) and their appearances. There are two methods for representations of such documents are Vector Space Model and Bag of words.
Feature Selection (attribute selection) − This approach results in providing low database space, minimal search methods by taking out irrelevant natures from the input document.
Data mining/Pattern Selection − In this process, the conventional data mining process combines with the text mining process. A structured database facilitates classic data mining techniques that resulted from an earlier stage.
Evaluate − This stage calculates the outcome. This resulting outcome can be focused away or can be used for the following set of sequences.
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