Ramnath Balasubramanyan

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We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contemporaneous Twitter messages. While our results vary across datasets, in several cases the correlations(More)
Identifying latent groups of entities from observed interactions between pairs of entities is a frequently encountered problem in areas like analysis of protein interactions and social networks. We present a model that combines aspects of mixed membership stochastic block models and topic models to improve entity-entity link modeling by jointly modeling(More)
Naive Bayes classifier has long been used for text categorization tasks. Its sibling from the unsupervised world, the probabilistic mixture of multinomial models, has likewise been successfully applied to text clustering problems. Despite the strong independence assumptions that these models make, their attractiveness come from low computational cost,(More)
We present a pseudo-observed variable based regularization technique for latent variable mixed-membership models that provides a mechanism to impose preferences on the characteristics of aggregate functions of latent and observed variables. The regularization framework is used to regularize topic models, which are latent variable mixed membership models for(More)
Email has become increasingly ubiquitous in recent times bringing with it new problems. In this paper we revisit two such problems, namely information leak detection and recipient recommendation, and study the impact of previously proposed solutions on real email users. Previous work addressing these problems showed a lot of promise on static email corpora(More)
Political discourse in the United States is getting increasingly polarized. This polarization frequently causes different communities to react very differently to the same news events. Political blogs as a form of social media provide an unique insight into this phenomenon. We present a multi-target, semisupervised latent variable model, MCR-LDA to model(More)
Microblogging services like Twitter are used for a wide variety of purposes and in different modes. Here, we focus on the usage of Twitter for <i>"chatter"</i> i.e., the production and consumption of tweets that are typically non-topical and contain personal status updates or conversational messages which are usually intended and are useful only to the(More)
Political blogs as a form of social media allow for an uniquely interactive form of political discourse. This is especially evident in focused blogs with a strong ideological identity. We investigate techniques to identify topics within the context of the community, which when discussed in a blog post evoke a discernible positive or negative collective(More)
We present methods to introduce different forms of supervision into mixed-membership latent variable models. Firstly, we introduce a technique to bias the models to exploit topic-indicative features, i.e. features which are apriori known to be good indicators of the latent topics that generated them. Next, we present methods to modify the Gibbs sampler used(More)