The additive clustering (ADCL US) model (Shepard & Arabie, 1979) treats the similarity of two stimuli as a weighted additive measure of their common features. Inspired by recent work in unsupervised learning with multiple cause models, we propose anew, statistically well-motivated algorithm for discovering the structure of natural stimulus classes using the ADCLUS model, which promises substant...

In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. We employ principal component analysis (PCA) to discover bipolar word usage patterns of person names in the documents and show that the signs of the entries in the principal eigenvector of PCA partition the person names into bipolar groups spontaneously. Empirical evaluations dem...

Sentiment Analysis is a discipline that aims at identifying and extract the subjectivity expressed by authors of information sources. Sentiment Analysis can be applied at different level of granularity and each of them still has open issues. In this paper we propose a completely unsupervised approach aimed at inducing a set of words patterns that change the polarity of subjective terms. This is...

1. Unsupervised learning. The authors’ emphasis is on the method as a useful way of representing data analogous to a wavelet representation where X = X(t) with t genuinely identified with a point on the line and observation at p time points, but where the time points have been permuted. As such, this can be viewed as a clustering method which, from their examples, gives very reasonable answers....

This paper describes our contributions to the Social Event Detection (SED) task as part of the MediaEval Benchmark 2014. We first present an unsupervised approach for the clustering of social events that builds solely on provided metadata. Results show that already the use of available time and location information achieves high clustering precision. In the next step, we focus on the retrieval ...

Much of opinion mining research focuses on product reviews because reviews are opinion-rich and contain little irrelevant information. However, this cannot be said about online discussions and comments. In such postings, the discussions can get highly emotional and heated with many emotional statements, and even personal attacks. As a result, many of the postings and sentences do not express po...

Leximancer is a software system for performing conceptual analysis of text data in a largely language independent manner. The system is modelled on Content Analysis and provides unsupervised and supervised analysis using seeded concept classifiers. Unsupervised ontology discovery is a key component.

Unsupervised Bayesian sentiment analysis often uses models that are not well motivated. Mostly, extensions of Latent Dirichlet Analysis (LDA) are applied – effectively modeling latent class distributions over words instead of documents. We introduce a Bayesian, unsupervised version of Naive Bayes for sentiment analysis and show that it offers superior accuracy and inference speed.