Abstract
In recent years, there is a rapid increased use of social networking platforms in the forms of short-text communication. Such communication can be indicative to popular public opinions and may be influential to real-life events. It is worth to identify topic groups from it automatically so it can help the analyst to understand the social network easily. However, due to the short-length of the texts used, the precise meaning and context of such texts are often ambiguous. In this paper, we proposed a hybrid framework, which adapts and extends the text clustering technique that uses Wikipedia as background knowledge. Based on this method, we are able to achieve higher level of precision in identifying the group of messages that has the similar topic.
Original language | English |
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Title of host publication | Agent and Multi-Agent Systems: Technologies and Applications |
Subtitle of host publication | 9th KES International Conference, KES-AMSTA 2015 Sorrento, Italy, June 2015, Proceedings |
Editors | Gordon Jezic, Robert J. Howlett, Lakhmi C. Jain |
Publisher | Springer |
Pages | 205-215 |
Number of pages | 11 |
Volume | Part IV |
ISBN (Electronic) | 978-3-319-19728-9 |
ISBN (Print) | 978-3-319-19727-2 |
DOIs | |
Publication status | Published - 2015 |
Event | 9th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications - Sorrento, Italy Duration: 17 Jun 2015 → 19 Jun 2015 |
Publication series
Name | Smart Innovation Systems and Technologies |
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Publisher | Springer International Publishing |
Volume | 38 |
ISSN (Print) | 2190-3018 |
Conference
Conference | 9th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications |
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Abbreviated title | KES-AMSTA 15 |
Country/Territory | Italy |
City | Sorrento |
Period | 17/06/15 → 19/06/15 |
Keywords
- Social Network Analysis
- Micro-blogging System
- Machine Learning
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Yun-Heh Chen-Burger
- School of Mathematical & Computer Sciences - Assistant Professor
- School of Mathematical & Computer Sciences, Computer Science - Assistant Professor
Person: Academic (Teaching)