An unsupervised multilingual approach for online social media topic identification
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 identify...
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sg-smu-ink.sis_research-58762020-02-13T08:50:11Z An unsupervised multilingual approach for online social media topic identification LO, Siaw Ling CHIONG, Raymond CORNFORTH, David 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. 2017-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4873 info:doi/10.1016/j.eswa.2017.03.029 https://ink.library.smu.edu.sg/context/sis_research/article/5876/viewcontent/An_unsupervised___PV.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University topic identification multilingual analysis unsupervised learning social media Computer Engineering Social Media |
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topic identification multilingual analysis unsupervised learning social media Computer Engineering Social Media LO, Siaw Ling CHIONG, Raymond CORNFORTH, David An unsupervised multilingual approach for online social media topic identification |
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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. |
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LO, Siaw Ling CHIONG, Raymond CORNFORTH, David |
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LO, Siaw Ling CHIONG, Raymond CORNFORTH, David |
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LO, Siaw Ling |
title |
An unsupervised multilingual approach for online social media topic identification |
title_short |
An unsupervised multilingual approach for online social media topic identification |
title_full |
An unsupervised multilingual approach for online social media topic identification |
title_fullStr |
An unsupervised multilingual approach for online social media topic identification |
title_full_unstemmed |
An unsupervised multilingual approach for online social media topic identification |
title_sort |
unsupervised multilingual approach for online social media topic identification |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2017 |
url |
https://ink.library.smu.edu.sg/sis_research/4873 https://ink.library.smu.edu.sg/context/sis_research/article/5876/viewcontent/An_unsupervised___PV.pdf |
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