Date of Award

2016

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Byung Suk S. Lee

Abstract

The uncontrolled usage of hashtags in social media makes them vary a lot in the quality of semantics and the frequency of usage. Such variations pose a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag by using metadata or the contextual semantics of a hashtag by using the texts associated with a hashtag. This thesis presents a hybrid approach to clustering hashtags based on their semantics, designed in two phases. The first phase is a sense-level metadata-based semantic clustering algorithm that has the ability to differentiate among distinct senses of a hashtag as opposed to the hashtag word itself. The gold standard test demonstrates that sense-level clusters are significantly more accurate than word-level clusters. The second phase is a hybrid semantic clustering algorithm using a consensus clustering approach which finds the consensus between metadata-based sense-level semantic clusters and text-based semantic clusters. The gold standard test shows that the hybrid algorithm outperforms both the text-based algorithm and the metadata-based algorithm for a majority of ground truths tested and that it never underperforms both baseline algorithms. In addition, a larger-scale performance study, conducted with a focus on disagreements in cluster assignments between algorithms, shows that the hybrid algorithm makes the correct cluster assignment in a majority of disagreement cases.

Language

en

Number of Pages

82 p.

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