News Headlines Classification Using Probabilistic Approach

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dc.contributor.author Mazhar iqbal rana
dc.contributor.author Dr. Shehzad khalid2
dc.contributor.author Fizza Abid
dc.contributor.author Armughan Ali
dc.contributor.author Mehr Yahya Durrani
dc.contributor.author Farhan Aadil
dc.date.accessioned 2017-11-22T07:56:31Z
dc.date.available 2017-11-22T07:56:31Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/123456789/5020
dc.description.abstract This paper is aimed at news classification on basis of their headlines. Researchers have worked a lot for carrying out news classification at full text level but work in the domain of news headlines classification exists in very limited ratio, Therefore, after analyzing variety of existing news classification methodologies, a probabilistic framework is presented in this paper for classifying news headlines. News headlines classification process is divided into three modules, headlines pre-processing module, probability learning module, and news headlines classification module. Based on availability of variety of headlines, probabilistic framework is designed, which classifies each news headline to its pre-defined category by calculating its maximum probability in that category. Work has been performed using bag of words approach where each headline is split into words and each word is given a certain probability. Furthermore, it is shown that proposed system gives better accuracy results as compared to existing headline classification systems. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.subject Department of Computer Engineering CE en_US
dc.title News Headlines Classification Using Probabilistic Approach en_US
dc.type Article en_US


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