[1402.6010] Tripartite Graph Clustering for Dynamic Sentiment …
Computer Science > Social and Information Networks
Title:
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media
Authors:Linhong Zhu
,
,
,
(Submitted on 24 Feb 2014 (
), last revised 26 Feb 2014 (this version, v2))
Abstract: The growing popularity of social media (e.g, Twitter) allows users to easily
share information with each other and influence others by expressing their own
sentiments on various subjects. In this work, we propose an unsupervised
emphtri-clustering framework, which analyzes both user-level and tweet-level
sentiments through co-clustering of a tripartite graph. A compelling feature of
the proposed framework is that the quality of sentiment clustering of tweets,
users, and features can be mutually improved by joint clustering. We further
investigate the evolution of user-level sentiments and latent feature vectors
in an online framework and devise an efficient online algorithm to sequentially
update the clustering of tweets, users and features with newly arrived data.
The online framework not only provides better quality of both dynamic
user-level and tweet-level sentiment analysis, but also improves the
computational and storage efficiency. We verified the effectiveness and
efficiency of the proposed approaches on the November 2012 California ballot
Twitter data.
Comments:
To be appeared in ACM SIGMOD’14, this is a longer version
Subjects:
Social and Information Networks (cs.SI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as:
arXiv:1402.6010 [cs.SI]
(or arXiv:1402.6010v2 [cs.SI] for this version)
Submission history
From: Linhong Zhu [
]
Mon, 24 Feb 2014 22:58:28 GMT (609kb)
[v2]
Wed, 26 Feb 2014 18:50:55 GMT (609kb)
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[1402.6010] Tripartite Graph Clustering for Dynamic Sentiment …
