Summarization algorithms performance for topic clustered twitter microblogs

Date of Award

2018

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Information Systems & Computer Science

First Advisor

Estuar, Ma. Regina Justina E., Ph.D.

Abstract

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.

Comments

The C7.S258 2018

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