Summarization algorithms performance for topic clustered twitter microblogs

This paper discusses an approach that would allow for the condensation of a bodyof Twitter microblogs into a wieldy size by extracting the topics being discussed in acorpus of tweets using Latent Dirichlet Allocation (LDA). The approach presents theoutput into a human readable summary using the Phra...

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Main Author: SANTOS, JOHN SIXTO G.
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Published: Archīum Ateneo 2018
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Online Access:https://archium.ateneo.edu/theses-dissertations/58
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1564945654&currentIndex=0&view=fullDetailsDetailsTab
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.theses-dissertations-10572021-03-21T12:30:03Z Summarization algorithms performance for topic clustered twitter microblogs SANTOS, JOHN SIXTO G. This paper discusses an approach that would allow for the condensation of a bodyof Twitter microblogs into a wieldy size by extracting the topics being discussed in acorpus of tweets using Latent Dirichlet Allocation (LDA). The approach presents theoutput into a human readable summary using the Phrase Reinforcement (PR)algorithm. The average F-measure score of this method exceeds those of othermethods when evaluated against human-made summaries. Results also suggest thatLDA together with PR is more robust against noisier datasets than the other testedmethods. This solution would help utilize Twitter into a tool not only for sharing ofexperiences but also a tool for gathering the state of the population. Decision makerscan use this solution to make informed action. 2018-01-01T08:00:00Z text https://archium.ateneo.edu/theses-dissertations/58 http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1564945654&currentIndex=0&view=fullDetailsDetailsTab Theses and Dissertations (All) Archīum Ateneo Twitter Corpora (Linguistics) -- Data processing Natural language processing (Computer science) Cluster analysis -- Computer programs.
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Twitter
Corpora (Linguistics) -- Data processing
Natural language processing (Computer science)
Cluster analysis -- Computer programs.
spellingShingle Twitter
Corpora (Linguistics) -- Data processing
Natural language processing (Computer science)
Cluster analysis -- Computer programs.
SANTOS, JOHN SIXTO G.
Summarization algorithms performance for topic clustered twitter microblogs
description This paper discusses an approach that would allow for the condensation of a bodyof Twitter microblogs into a wieldy size by extracting the topics being discussed in acorpus of tweets using Latent Dirichlet Allocation (LDA). The approach presents theoutput into a human readable summary using the Phrase Reinforcement (PR)algorithm. The average F-measure score of this method exceeds those of othermethods when evaluated against human-made summaries. Results also suggest thatLDA together with PR is more robust against noisier datasets than the other testedmethods. This solution would help utilize Twitter into a tool not only for sharing ofexperiences but also a tool for gathering the state of the population. Decision makerscan use this solution to make informed action.
format text
author SANTOS, JOHN SIXTO G.
author_facet SANTOS, JOHN SIXTO G.
author_sort SANTOS, JOHN SIXTO G.
title Summarization algorithms performance for topic clustered twitter microblogs
title_short Summarization algorithms performance for topic clustered twitter microblogs
title_full Summarization algorithms performance for topic clustered twitter microblogs
title_fullStr Summarization algorithms performance for topic clustered twitter microblogs
title_full_unstemmed Summarization algorithms performance for topic clustered twitter microblogs
title_sort summarization algorithms performance for topic clustered twitter microblogs
publisher Archīum Ateneo
publishDate 2018
url https://archium.ateneo.edu/theses-dissertations/58
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1564945654&currentIndex=0&view=fullDetailsDetailsTab
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