Informative feature discovery for social media mining
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Khaled Mohammed H Albishre Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
Finding relevant information in social media data to satisfy the user need presents unique challenges due to its nature (e.g. high volume, short length, sparseness). This thesis aims to discover informative feature representations that can help to capture user information needs when no annotated data is available. Using state-of-the-art techniques in text mining and information retrieval research, this research proposes novel methods to boost the user information need with representative information in a social media context. The experimental results show that the proposed models outperform baseline models on standard TREC 2011-2014 microblog datasets.
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| ID Code: | 199464 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Li, Yuefeng & Xu, Yue |
| Keywords: | Information Retrieval, Microblog Retrieval, Social Media Mining, Social Search, Query Expansion, Relevance Ranking, Topic Modeling, Text Mining |
| DOI: | 10.5204/thesis.eprints.199464 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Past > Schools > School of Electrical Engineering & Computer Science |
| Institution: | Queensland University of Technology |
| Deposited On: | 22 May 2020 06:48 |
| Last Modified: | 22 May 2020 06:48 |
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