Topic Analysis to Identify Communities

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dc.contributor.authorJohansson, Algot
dc.contributor.authorGuldbrand, Eric
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerMostad, Petter
dc.contributor.supervisorJonasson, Johan
dc.date.accessioned2021-06-16T08:53:25Z
dc.date.available2021-06-16T08:53:25Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractAbstract Being able to detect communities in social networks can be an aid in understanding trends, assist moderation efforts and build recommendation systems. In this paper we explore the use of topic models for community detection by proposing two such models, LDAC and LDACS, based off of Latent Dirichlet Allocation (LDA) [1] and the Community Topic Model [8]. These models are compared to LDA and evaluated on datasets collected from Twitter and Reddit. It is concluded that LDACS may be a reasonable and simple model for community detection, but with further study needed, and that LDAC gives some credence to utilizing both topics and communities in a model, but does itself not produce sufficient results to weigh up for its complexity, although training it on more data might remedy this.sv
dc.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/302554
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjecttopic analysis, community detection, community, topic, thesis, lda, ldac, ldacs, ctm.sv
dc.titleTopic Analysis to Identify Communitiessv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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