Bilinear regression

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Lampos, Vasileios, Daniel Preoţiuc-Pietro, Sina Samangooei, Douwe Gelling, and Trevor Cohn. Extracting socioeconomic patterns from the news: Modelling text and outlet importance jointly In Workshop on Language Technologies and Computational Social Science (LACSS). ACL., 2014. AbstractPDFPoster

Information from news articles can be used to study correlations between textual discourse and socioeconomic patterns. This work focuses on the task of understanding how words contained in the news as well as the news outlets themselves may relate to a set of indicators, such as economic sentiment or unemployment rates. The bilinear nature of the applied regression model facilitates learning jointly word and outlet importance, supervised by these indicators. By evaluating the predictive ability of the extracted features, we can also assess their relevance to the target socioeconomic phenomena. Therefore, our approach can be formulated as a potential NLP tool, particularly suitable to the computational social science community, as it can be used to interpret connections between vast amounts of textual content and measurable society driven factors.

Lampos, Vasileios, Daniel Preoţiuc-Pietro, and Trevor Cohn. A user-centric model of voting intention from Social Media. ACL., 2013. AbstractPDFPoster

Social Media contain a multitude of user opinions which can be used to predict realworld phenomena in many domains including politics, finance and health. Most existing methods treat these problems as linear regression, learning to relate word frequencies and other simple features to a known response variable (e.g., voting intention polls or financial indicators). These techniques require very careful filtering of the input texts, as most Social Media posts are irrelevant to the task. In this paper, we present a novel approach which performs high quality filtering automatically, through modelling not just words but also users, framed as a bilinear
model with a sparse regulariser. We also consider the problem of modelling groups of related output variables, using a structured multi-task regularisation method. Our experiments on voting intention prediction demonstrate strong performance over large-scale input from Twitter on two distinct case studies, outperforming competitive baselines.