Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2017

Abstract

Social media data can be valuable in many ways. However, the vast amount of content shared and the linguistic variants of languages used on social media are making it very challenging for high-value topics to be identified. In this paper, we present an unsupervised multilingual approach for identifying highly relevant terms and topics from the mass of social media data. This approach combines term ranking, localised language analysis, unsupervised topic clustering and multilingual sentiment analysis to extract prominent topics through analysis of Twitter’s tweets from a period of time. It is observed that each of the ranking methods tested has their strengths and weaknesses, and that our proposed ‘Joint’ ranking method is able to take advantage of the strengths of the ranking methods. This ‘Joint’ ranking method coupled with an unsupervised topic clustering model is shown to have the potential to discover topics of interest or concern to a local community. Practically, being able to do so may help decision makers to gauge the true opinions or concerns on the ground. Theoretically, the research is significant as it shows how an unsupervised online topic identification approach can be designed without much manual annotation effort, which may have great implications for future development of expert and intelligent systems.

Keywords

topic identification, multilingual analysis, unsupervised learning, social media

Discipline

Computer Engineering | Social Media

Research Areas

Data Science and Engineering

Publication

Expert Systems with Applications

Volume

81

First Page

282

Last Page

298

ISSN

0957-4174

Identifier

10.1016/j.eswa.2017.03.029

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.eswa.2017.03.029

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