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Summarization algorithms performance for topic clustered twitter microblogs

Bibliographic Details
Title: Summarization algorithms performance for topic clustered twitter microblogs
Authors: SANTOS, JOHN SIXTO G.
Source: Theses and Dissertations (All)
Publisher Information: Archīum Ateneo
Publication Year: 2018
Subject Terms: Twitter, Corpora (Linguistics) -- Data processing, Natural language processing (Computer science), Cluster analysis -- Computer programs
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.
Document Type: text
Language: unknown
Relation: http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1564945654&currentIndex=0&view=fullDetailsDetailsTab
Availability: https://archium.ateneo.edu/theses-dissertations/58
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1564945654&currentIndex=0&view=fullDetailsDetailsTab
Accession Number: edsbas.53FE1C90
Database: BASE
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  – Url: https://archium.ateneo.edu/theses-dissertations/58#
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  Data: Summarization algorithms performance for topic clustered twitter microblogs
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  Data: <searchLink fieldCode="AR" term="%22SANTOS%2C+JOHN+SIXTO+G%2E%22">SANTOS, JOHN SIXTO G.</searchLink>
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  Data: Theses and Dissertations (All)
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  Data: 2018
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  Data: <searchLink fieldCode="DE" term="%22Twitter%22">Twitter</searchLink><br /><searchLink fieldCode="DE" term="%22Corpora+%28Linguistics%29+--+Data+processing%22">Corpora (Linguistics) -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing+%28Computer+science%29%22">Natural language processing (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+--+Computer+programs%22">Cluster analysis -- Computer programs</searchLink>
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  Label: Description
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  Data: 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.
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    Subjects:
      – SubjectFull: Twitter
        Type: general
      – SubjectFull: Corpora (Linguistics) -- Data processing
        Type: general
      – SubjectFull: Natural language processing (Computer science)
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      – SubjectFull: Cluster analysis -- Computer programs
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      – TitleFull: Summarization algorithms performance for topic clustered twitter microblogs
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