Anthony Brew

Using Crowdsourcing and Active Learning to Track Sentiment in Online Media

Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource Crowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community. much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.

Combining cohort and UBM models in open set speaker detection

In speaker detection it is important to build an alternative model against which to compare scores from the ‘target’ speaker model. Two alternative strategies for building an alternative model are to build a single global model by sampling from a pool of training data, the Universal Background (UBM), or to build a cohort of models from selected individuals in the training data for the target speaker. The main contribution in this paper is to show that these approaches can be unified by using a Support Vector Machine (SVM) to learn a decision rule in the score space made up of the output scores of the client, cohort and UBM model.

Vector Quantization Mappings for Speaker Verification

In speaker verification several techniques have emerged to map variable length utterances into a fixed dimensional space for classification. One popular approach uses Maximum A-Posteriori (MAP) adaptation of a Gaussian Mixture Model (GMM) to create a super-vector. This paper investigates using Vector Quantisation (VQ) as the global model to provide a similar mapping. This less computationally complex mapping gives comparable results to its GMM counterpart while also providing the ability for an efficient iterative update enabling media files to be scanned with a fixed length window.

A machine learning system for tracking sentiment in irish economic news

Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to “crowdsource” much of the annotation work that is required for the construction of predictive models.