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Text classification using machine learning algorithms
2021-07-07
Text classification using machine learning algorithms
In the latest ten years' content-based document management tasks (collectively indicate to as information retrieval – IR) have gained obvious standing inside the information systems field, due to increase the availability of documents in digital convenience to arrival them in flexible ways. With the fast growth of the internet and online knowledge, automatic text classification has attracted many researchers and corporations. There are several numbers of algorithms for machine learning has been applied to text classification, for example: decision trees, k-nearest-neighbor , Naive-Bayes and SVM. The studies of text mining are obtaining more importance, recently because of the accessibility of the increasing number of the electronic documents from kinds of sources that contain unstructured and semi-structured information. Major goals of text mining is to let users extract the information from textual resources and treat with the operations such as, information Retrieval, categorization (supervised, unsupervised and semi-supervised) and Natural Language Processing (NLP), Data Mining, and Machine Learning (ML) techniques work together to automatically classify and discover patterns from the various kinds of the documents Text classification (TC) is an important part of text mining (TM), text classification as well known "text categorization" is the operation of arranging text documents for one or additional predefined groups, in another meaning to enter the class labels to documents. It is also called topic spotting; it can also be seen as the problem of spotting the topic of text documents. Automatic text classification assume an imperative part in a wide variety of more flexible, effective and personalized information arrangement task
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