Analysis of graph and text representation techniques for news recommendation and news classification

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Analysis_of_Graph_and_Text_Representation_Techniques_for_News_Recommendation_and_News_Classification.pdf

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2022-2-07

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Ağrıman, Mustafa

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Developments in computer science leads to increase in the use of software applications in all areas of life. This also causes an increase in data usage. Applications using textual data involves tasks such as finding similarities between texts, detecting events from texts, and classifying texts. However, using graphs and graph vectors can be more successful than textual methods of representing textual information, due to capability to express additional features and complex relationships in graph structure. In this thesis, it is hypothesized that textual data expresse in graph structure will be more successful than direct text representation in areas such as news recommendation and news classification. Within the scope of the thesis study, different graph representation methods have been applied and the results obtained from these methods have been compared with the performance under text representations.

Subject Keywords

Graph, Graph embedding, Graph mining, News recommendation, News classification

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https://hdl.handle.net/11511/96321

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. Ağrıman, “Analysis of graph and text representation techniques for news recommendation and news classification,” M.S. - Master of Science, Middle East Technical University, 2022.