2026-09-19T05:37:03Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1401092022-02-08T03:12:40Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Roy, Deb
Sun, Daniel X.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-02-07T15:24:38Z
2022-02-07T15:24:38Z
2021-09
2021-11-03T19:25:33.170Z
https://hdl.handle.net/1721.1/140109
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.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Clustering Tweets via Tweet Embeddings
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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.</Abstract>
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