Using a probabilistic approach for exploring latent patterns in high-dimensional co-
occurrence data, topic models offer researchers a flexible and open framework for soft …

Sentiment analysis, a specialization of natural language processing (NLP), has witnessed
significant progress since its emergence in the late 1990s, owing to the swift advances in …

Twitter as a new form of social media can potentially contain much useful information, but
content analysis on Twitter has not been well studied. In particular, it is not clear whether as …

By aggregating self-reported health statuses across millions of users, we seek to
characterize the variety of health information discussed in Twitter. We describe a topic …

Aspect-level sentiment analysis or opinion mining consists of several core sub-tasks: aspect
extraction, opinion identification, polarity classification, and separation of general and aspect …

User generated content is the basic element of social media websites. Relatively few studies
have systematically analyzed the motivation to create and share content, especially from the …

This paper presents a novel opinion mining research problem, which is called Contrastive
Opinion Modeling (COM). Given any query topic and a set of text collections from multiple …

This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly
discovers topics and aspects. We broadly define an aspect of a document as a characteristic …

Cross-collection topic models extend previous single-collection topic models, such as Latent
Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic …

Social media streams such as Twitter are regarded as faster first-hand sources of
information generated by massive users. The content diffused through this channel …