On the use of ensemble method for multi view textual data

• Main Authors: Maha Fraj, Mohamed Aymen Ben Hajkacem, Nadia Essoussi
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-10-01
• Series: Journal of Information and Telecommunication
• Subjects: text clustering; multi-view data; ensemble method; skip-gram; lda
• Online Access: http://dx.doi.org/10.1080/24751839.2020.1765117

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.