An unsupervised multilingual approach for online social media topic identification
作者：
Highlights：
• An unsupervised multilingual approach to identify topics on Twitter is proposed.
• Localised language can be leveraged for identifying relevant and important topics.
• ‘Joint’ term ranking coupled with DPMM clustering consistently performed well.
• Multilingual sentiment analysis is essential to understand sentiment on the ground.
• Topics coverage of social media and main stream media does not always stay the same.
摘要
•An unsupervised multilingual approach to identify topics on Twitter is proposed.•Localised language can be leveraged for identifying relevant and important topics.•‘Joint’ term ranking coupled with DPMM clustering consistently performed well.•Multilingual sentiment analysis is essential to understand sentiment on the ground.•Topics coverage of social media and main stream media does not always stay the same.
论文关键词：Topic identification,Multilingual analysis,Unsupervised learning,Social media
论文评审过程：Received 12 July 2016, Revised 13 March 2017, Accepted 14 March 2017, Available online 21 March 2017, Version of Record 2 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.029
