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Distinguishing between factual information and insulting or abusive messages bearing words or phrases in news articles

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dc.contributor.advisor Khan, Mumit Mahmud, Altaf Ahmed, Kazi Zubair 2010-09-19T05:18:31Z 2010-09-19T05:18:31Z 2006 2006-08
dc.identifier.other ID 02201117
dc.identifier.other ID 02101119
dc.description This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2006. en_US
dc.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 75).
dc.description.abstract Since Internet has become the leading source of information for the users, flames or abusive messages have also become the prominent factors of time wasting for retrieving information. Moreover, a text can contain factual information as well as abusive or insulting contents. This paper describes a new approach for an automated system to distinguish between information and personal attack containing insulting or abusive messages in a given document. In NLP, flames or abusive messages are considered as extreme subjective language, which refers to detect personal opinions or emotions in a news article. Insulting or abusive messages are viewed as extreme subset of the subjective language because of its extreme nature. We defined some rules to extract the semantic information of a given sentence from the general semantic structure of that sentence. en_US
dc.description.statementofresponsibility Altaf Mahmud
dc.description.statementofresponsibility Kazi Zubair Ahmed
dc.format.extent 84 pages
dc.language.iso en
dc.publisher BRAC University en_US
dc.rights BRAC University thesis 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 Computer science and engineering
dc.title Distinguishing between factual information and insulting or abusive messages bearing words or phrases in news articles en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University B. Computer Science and Engineering

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