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dc.contributorDepartment of Computingen_US
dc.creatorCheng, Ming Fung-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleGPU accelerated hot term extraction from user generated contenten_US
dcterms.abstractThis thesis aims at developing and investigating an efficient approach to hot term extraction. In the Web 2.0, the user generated content (UGC) is increased dramatically in different Consumer Generated Media (CGM) such as forums and blogs. People easily search their knowledge and opinions in CGM as well as generate Word Of Mouth (WOM) in different online channels. Facing the huge amount of data, it is not easy to find the useful information even using a search engine. Having a good hot term extraction algorithm can reveal hidden information to users and also provide an indicator in the search results, so that users can easily know which terms are popular in the search results. In this thesis, a GPU based hot term extraction algorithm is presented. Graphics Processing Units (GPUs) is designed for data-parallel computations. Comparing to running a single program with multiple data in CPU, GPU can have faster execution. The hot term is defined as a word that appears frequently in the search result. We assume that the greater the frequency of appearance of a term, the more the relevancy of the term to the users. As there are lots of terms in the searched results, processing them is time-consuming. The proposed GPU based hot term extraction algorithm can achieve a fast performance and works well in real-time applications.en_US
dcterms.extent102 leaves : ill. (some col.) ; 30 cm.en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHInternet searching.en_US
dcterms.LCSHInformation retrieval.en_US
dcterms.LCSHInformation storage and retrieval systems -- Mathematical models.en_US
dcterms.LCSHGraphics processing units.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/6745