MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Clustering Tweets via Tweet Embeddings

Author(s)
Sun, Daniel X.
Thumbnail
DownloadThesis PDF (944.7Kb)
Advisor
Roy, Deb
Terms of use
In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/
Metadata
Show full item record
Abstract
Twitter is a popular social media platform where users interact through follows and tweets. This work explores computational methods of analyzing tweets with regards to understanding users and their interests. We consider various embedding models to produce tweet embeddings, which we then use to cluster the tweets, forming groups of semantically similar tweets. We then compare these tweet clusters to users clustered by interest based on accounts they follow. This work introduces techniques on how to effectively cluster tweets by semantic meaning despite the colloquial structure of tweet language. We also discuss how the topics of these tweet clusters align with the interests derived from the follow-based clustering approach, and provide insights into where they do and don’t intersect.
Date issued
2021-09
URI
https://hdl.handle.net/1721.1/140109
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.