Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social
  Media   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
\emph{tri-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.

12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex