On the use of ensemble method for multi view textual data
Nowadays, trends detection is an important task on social media to determine trends that are being discussed the most on a social platform. One of the main challenges of this task is the processing of unstructured textual data which has different representations. Therefore, multi view text clusterin...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2020-10-01
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Series: | Journal of Information and Telecommunication |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/24751839.2020.1765117 |
Summary: | Nowadays, trends detection is an important task on social media to determine trends that are being discussed the most on a social platform. One of the main challenges of this task is the processing of unstructured textual data which has different representations. Therefore, multi view text clustering presents a useful solution for trends detection by integrating various representations called ‘views’ to provide a complementary description of the same content. In this context, we propose a new ensemble method for multi-view text clustering that exploits different representations of text in order to produce more accurate and high quality clustering. Extensive experiments on real-world text datasets were conducted to demonstrate its superiority by comparing with the existing methods. An application of the proposed method in trends detection from twitter is also illustrated. |
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ISSN: | 2475-1839 2475-1847 |