Clustering Tweets via Tweet Embeddings
Name
Sun-dxsun-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
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944.76 KB
Format
Adobe PDF
Checksum (MD5)
d1aab028a32e577c472290c40330198c
Author(s)
Sun, Daniel X.
Advisor(s)
Roy, Deb
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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