Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media

Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media. Zhu, L., Galstyan, A., Cheng, J., & Lerman, K. In Proceedings of the ACM SIGMOD/PODS, 2014.

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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.

@inproceedings{Zhu14sigmod,
    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
\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.},
    author = {Zhu, Linhong and Galstyan, Aram and Cheng, James and Lerman, Kristina},
    booktitle = {Proceedings of the ACM SIGMOD/PODS},
    keywords = {social-networks},
    title = {Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media},
    urlPaper = {http://arxiv.org/abs/1402.6010},
    year = {2014}
}

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