Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/41703
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Gonçalves, Nuno Miguel Mendonça da Silva | - |
dc.contributor.author | Ugalde, Diego Salas | - |
dc.date.accessioned | 2017-06-01T16:53:21Z | - |
dc.date.available | 2017-06-01T16:53:21Z | - |
dc.date.issued | 2015-07 | - |
dc.identifier.uri | https://hdl.handle.net/10316/41703 | - |
dc.description.abstract | Internet keeps growing everyday and with that, the creation of new web pages. Due to this fact, web pages of many different categories can be found such as News, Sports or Business. This issue has made investigators think about one innovative concept: Webpage Classification. This new approach implies the categorization of web pages to one or more category labels. Some research has been done during the last years using text and visual content extracted from the web pages to be able to classify. However, the need of being able to do such a thing in an Android app has not been investigated yet, to the best of our knowledge. Consequently, this thesis is focused in the development of an Android app which is able to classify web pages. First of all, text and visual features have to be extracted from each webpage. Four types of visual features were extracted from each web page to construct a visual features vector of 160 attributes. Concerning to the text features, a text features vector was also built for each of the webpage with 160 attributes. To do so, a “Bag-Of-Words” of one hundred and sixty words was set up from the HTML code already extracted and filtered. Thus, we end up having a full vector of 320 attributes for each webpage. A binary classification was performed trying to distinguish web pages for Adults and for Kids. Good results were obtained especially when using AdaBoost classifier with text and visual features where a 94.44% of accuracy of correct classifications was achieved. | por |
dc.language.iso | eng | por |
dc.rights | openAccess | por |
dc.subject | Páginas web | por |
dc.subject | Classificação | por |
dc.subject | Aplicação Android | por |
dc.title | Android app for Automatic Web Page Classification : Analysis of Text and Visual Features | por |
dc.type | masterThesis | por |
dc.peerreviewed | no | por |
dc.subject.fos | Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
thesis.degree.grantor | 00500::Universidade de Coimbra | por |
thesis.degree.name | Mestrado em Engenharia Eletrotécnica e de Computadores | - |
uc.controloAutoridade | Sim | - |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | masterThesis | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.advisor.researchunit | ISR - Institute of Systems and Robotics | - |
crisitem.advisor.parentresearchunit | University of Coimbra | - |
crisitem.advisor.orcid | 0000-0002-1854-049X | - |
Appears in Collections: | UC - Dissertações de Mestrado FCTUC Eng.Electrotécnica - Teses de Mestrado |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FINAL THESIS.pdf | 1.92 MB | Adobe PDF | View/Open |
Page view(s) 50
603
checked on Oct 1, 2024
Download(s) 20
1,276
checked on Oct 1, 2024
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.