dc.contributor.advisor | Chakrabarty, Amitabha | |
dc.contributor.advisor | Alam, Md. Golam Rabiul | |
dc.contributor.author | Mahmud, Abdullah Al | |
dc.contributor.author | Noor, Jannat-E | |
dc.contributor.author | Reshad, Sadman Alam | |
dc.contributor.author | Fuad, Syed Nafis | |
dc.date.accessioned | 2021-09-06T06:28:35Z | |
dc.date.available | 2021-09-06T06:28:35Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021-06 | |
dc.identifier.other | ID 17301033 | |
dc.identifier.other | ID 17101021 | |
dc.identifier.other | ID 17101403 | |
dc.identifier.other | ID 17101250 | |
dc.identifier.uri | http://hdl.handle.net/10361/14976 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 20-21). | |
dc.description.abstract | Text Documents often contain valuable data. But not all data is relevant. That is
why extracting relevant data from text documents is an essential task. Extracting
relevant data from text documents refers to the study of classifying text documents
into such groups that describe the contents of documents. There are many methods
to find out relevant data from a cluster of text or a text document. Classifying
extensive textual data helps to organize the records better, make the search easier
and relevant and simplify navigation. That makes this task still an open research
issue. This paper uses three techniques of classifying text documents: convolution
neural networks (CNN) with deep learning, Gaussian Na¨ıve Bayes and support vector machines (SVM). With these three algorithms, the text we want to classify goes
through three layers of checks. So, it gives us more reliability. | en_US |
dc.description.statementofresponsibility | Abdullah Al Mahmud | |
dc.description.statementofresponsibility | Jannat-E-Noor | |
dc.description.statementofresponsibility | Sadman Alam Reshad | |
dc.description.statementofresponsibility | . Syed Nafis Fuad | |
dc.format.extent | 22 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | CNN | en_US |
dc.subject | SVM | en_US |
dc.subject | Gaussian Na¨ıve Bayes | en_US |
dc.subject | Text classification | en_US |
dc.subject.lcsh | Machine learning | |
dc.title | What is relevant in a text document a machine learning based approach | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |