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Machine Learning Text Analyzer – Text Classification using Supervised and Un-Supervised Algorithms

dc.contributor.authorEzeaneche, Ifeoma Amaranna
dc.date.accessioned2019-08-08T10:55:10Z
dc.date.available2019-08-08T10:55:10Z
dc.date.issued2019-06-16
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/4899
dc.description.abstractText analysis is a branch of data mining that deals with text documents. This project brings to light the classification of texts into their various categories. The structured and unstructured data seems to on a high rise in this era. Thus, to be able to classify this data is important. Classification however starts from collection, preprocessing, and feature extraction. There are several techniques that can be used for text classification, but machine learning algorithms will be employed in this project. Because of the advent of Natural Language Processing, we will be able to see the need for feature extraction and selection. In this research, we will be able to see how the computer intelligently classifies text into their various categories. Emphasis will be on English language word document.en_US
dc.description.sponsorshipAUST and AfDB.en_US
dc.language.isoenen_US
dc.subjectEzeaneche, Ifeoma Amarannaen_US
dc.subject2019 Computer Science and Engineering Thesesen_US
dc.subjectProf. Ekpe Okoraforen_US
dc.subjectMatching Algorithms (ML)en_US
dc.subjecttext classificationen_US
dc.subjectMNBen_US
dc.subjectKNNen_US
dc.subjectSVMen_US
dc.subjectLRen_US
dc.titleMachine Learning Text Analyzer – Text Classification using Supervised and Un-Supervised Algorithmsen_US
dc.typeThesisen_US


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    This collection contains Computer Science Student's Theses from 2009-2022

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