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https://ptsldigital.ukm.my/jspui/handle/123456789/476484Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Sabrina Tiun, Dr. | - |
| dc.contributor.author | Thamer Saleh Tuwaya (P80532) | - |
| dc.date.accessioned | 2023-10-06T09:19:20Z | - |
| dc.date.available | 2023-10-06T09:19:20Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.other | ukmvital:107073 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476484 | - |
| dc.description | With the exponential growth of textual information available from the Internet, there has been an emergent need to find relevant, in-time and in-depth knowledge about Business topic. The huge size of such data makes the process of retrieving analyzing and use of the valuable information in such texts, manually a very difficult task. In this research, we attempt to address a challenging task i.e. a crawling business-specific knowledge on the Web. Thus, the main goal of this study is to describe a new method of focused crawler for online Business web pages based on latent semantic indexing. This study will describe a new model for online Business text crawling which seeks, acquires, maintains and filter Business web pages. This model consists mainly from two main modules: a crawling system and a text filtering system.The crawler is used to collect as many web pages as possible from the News websites. This focused crawler is guided by a latent semantic index and information from WordNet (Business filter) which learns to recognize the relevance of a web page with respect to the Business topic and it also utilizes a set of domain specific keywords. Several models for Business webpages classification has been designed and evaluated using latent sematic indexing based on two weighting methods; Term Frequency (TF) and Term Frequency x Inverse Document Frequency (TF.IDF); The obtained results showed that latent semantic indexing with TF.IDF weighting achieved the best performance with an F-measure (92.6%) on Business webpages classification. The obtained results on online real world data also show that the focused crawler using latent semantic indexing with TFIDF weighting is very effective for building high-quality collections of Business web documents.,“Certification of Master's/Doctoral Thesis” is not available,Master of Information Technology | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | - |
| dc.rights | UKM | - |
| dc.subject | Latent semantic indexing | - |
| dc.subject | Web search engines | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.title | Focused crawling of online business web pages using enhanced latent semantic indexing approach | - |
| dc.type | theses | - |
| dc.format.pages | 64 | - |
| dc.identifier.callno | TK5105.884.T848 2017 3 tesis | - |
| dc.identifier.barcode | 003802(2019) | - |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_107073+SOURCE1+SOURCE1.0.PDF Restricted Access | 937.77 kB | Adobe PDF |  View/Open |
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