Tweets clustering using latent semantic analysis

Norsuhaili Mahamed Rasidi, Sakhinah Abu Bakar, Fatimah Abdul Razak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social media are becoming overloaded with information due to the increasing number of information feeds. Unlike other social media, Twitter users are allowed to broadcast a short message called as 'tweet". In this study, we extract tweets related to MH370 for certain of time. In this paper, we present overview of our approach for tweets clustering to analyze the users' responses toward tragedy of MH370. The tweets were clustered based on the frequency of terms obtained from the classification process. The method we used for the text classification is Latent Semantic Analysis. As a result, there are two types of tweets that response to MH370 tragedy which is emotional and non-emotional. We show some of our initial results to demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publication4th International Conference on Mathematical Sciences - Mathematical Sciences
Subtitle of host publicationChampioning the Way in a Problem Based and Data Driven Society, ICMS 2016
PublisherAmerican Institute of Physics Inc.
Volume1830
ISBN (Electronic)9780735414983
DOIs
Publication statusPublished - 27 Apr 2017
Event4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016 - Putrajaya, Malaysia
Duration: 15 Nov 201617 Nov 2016

Other

Other4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016
CountryMalaysia
CityPutrajaya
Period15/11/1617/11/16

Fingerprint

semantics
messages

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Rasidi, N. M., Abu Bakar, S., & Abdul Razak, F. (2017). Tweets clustering using latent semantic analysis. In 4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016 (Vol. 1830). [020060] American Institute of Physics Inc.. https://doi.org/10.1063/1.4980923

Tweets clustering using latent semantic analysis. / Rasidi, Norsuhaili Mahamed; Abu Bakar, Sakhinah; Abdul Razak, Fatimah.

4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016. Vol. 1830 American Institute of Physics Inc., 2017. 020060.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rasidi, NM, Abu Bakar, S & Abdul Razak, F 2017, Tweets clustering using latent semantic analysis. in 4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016. vol. 1830, 020060, American Institute of Physics Inc., 4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016, Putrajaya, Malaysia, 15/11/16. https://doi.org/10.1063/1.4980923
Rasidi NM, Abu Bakar S, Abdul Razak F. Tweets clustering using latent semantic analysis. In 4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016. Vol. 1830. American Institute of Physics Inc. 2017. 020060 https://doi.org/10.1063/1.4980923
Rasidi, Norsuhaili Mahamed ; Abu Bakar, Sakhinah ; Abdul Razak, Fatimah. / Tweets clustering using latent semantic analysis. 4th International Conference on Mathematical Sciences - Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society, ICMS 2016. Vol. 1830 American Institute of Physics Inc., 2017.
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