Robertson, Dave

Chen-Burger, Yun-Heh

Yang, Cheng-lin

2020-06-23T11:19:43Z

2020-06-23T11:19:43Z

2020-06-25

In recent years, topic group mining and massive crowd opinion analysis from on-line social network platforms have become some of the most important tasks not only in research area but also in industry. Systems of this sort can identify similar topics from a very large dataset, group them together based on the topic, and analyse the inclination of the content's owner. To solve this problem, which involves research from a number of different areas, an integrated platform needs to be proposed. Most community mining techniques treat the network as a graph where nodes represent users and edges reflect user relationship between two users. One obvious drawback of these approaches is that it can only utilise the explicit user relationships provided by on-line social network platforms. All other possible relationships will be ignored. Some on-line social network platforms restrict the length of content a user can publish. This causes traditional document clustering methods to perform poorly. Meanwhile, the restriction of content length also affects opinion mining performance since most content lacks contextual features. Hence, other context features that are not immediately or obviously related need to be investigated to improve performance in user inclination classification. This research proposes a novel three layered platform. Two core technologies of the platform are topic group mining and user inclination analysis. The integrated approach was evaluated by a series of experiments to examine each core technology. The results indicate that the proposed integrated platform is able to produce the following results. 1) Scores up to 0.82 by V-measure evaluation function in topic group mining. 2) High accuracy rate in inclination mining. 3) A flexible and adaptable platform design which can accommodate different on-line social networks easily.

https://hdl.handle.net/1842/37178

http://dx.doi.org/10.7488/era/479

en

The University of Edinburgh

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C.-L.Yang, N. Benjamasutin, and Y.-H.Chen-Burger. Mining hidden concepts: Using short text clustering and wikipedia knowledge. In Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on, pages 675–680. IEEE, 2014.

C.-L. Yang and Y.-H. Chen-Burger. A hybrid on-line topic groups mining platform. In Agent and Multi-Agent Systems. Technologies and Applications. Springer, 2015.

G. Nadarajan, C.-L.Yang and Y.-H. Chen-Burger, ”Chapter 9 -Intelligent Workflow Management for Fish4 Knowledge using the SWELL System” in Springer Big Data for Marine Biology – A F4K Story – An Integrated Interdisciplinary Computational Approach

G. Nadarajan, C.-L.Yang and Y.-H. Chen-Burger, ”Appendix - Database Tables Related to F4K Workflow” in Springer Big Data for Marine Biology – AF4K Story – An Integrated Interdisciplinary Computational Approach

G. Nadarajan, C.-L. Yang, Y.-H. Chen-Burger. ”Multiple Ontologies Enhanced with Performance Capabilities to Define Interacting Domains within a Workflow Framework for Analysing Large Undersea Videos”, 5th International Conference on Knowledge Engineering and Ontology Development (KEOD 2013), Portugal, Sept 2013.

G. Nadarajan, C.-L.Yang, Y.-J. Cheng, S.-I.Lin, Y.-H. Chen-Burger, F.-P. Lin. ”Real-time Data Streaming Architecture and Intelligent Workflow Management for Processing Massive Ecological Videos.” International Conference on Social Computing (SocialCom) 2013, pp. 1074-1080, Washington, USA, Sept 2013

topic group mining

massive crowd opinion analysis

social network analysis

accuracy rate

integrated platforms

community mining techniques

Novel platform for topic group mining, crowd opinion analysis and opinion leader identification in on-line social network platforms

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy