27,167 research outputs found
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.Comment: A short version is in Proceeding of the 2014 ACM SIGMOD International
Conference on Management of dat
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets
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