Text regression

Flekova, Lucie, Lyle Ungar, and Daniel Preoţiuc-Pietro. Exploring Stylistic Variation with Age and Income on Twitter. ACL., 2016. AbstractPDFSlides

Writing style allows NLP tools to adjust to the traits of an author. In this paper, we explore the relation between stylistic and syntactic features and authors’ age and income. We confirm our hypothesis that for numerous feature types writing style is predictive of income even beyond age. We analyze the predictive power of writing style features in a regression task on two data sets of around 5,000 Twitter users each. Additionally, we use our validated features to study daily variations in writing style of users from distinct income groups. Temporal stylistic patterns not only provide novel psychological insight into user behavior, but are useful for future research and applications in social media.

Preoţiuc-Pietro, Daniel, Wei Xu, and Lyle Ungar. Discovering User Attribute Stylistic Differences via Paraphrasing In AAAI., 2016. AbstractPDFSlides

User attribute prediction from social media text has proven successful and useful for downstream tasks. In previous studies, user trait differences have been limited primarily to the presence or absence of words that indicate topical preferences. In this study, we aim to find linguistic style distinctions across three different user attributes: gender, age and occupational class. By combining paraphrases with a simple yet effective method, we capture a wide set of stylistic differences that are exempt from topic bias. We show their predictive power in user profiling, conformity with human perception and psycholinguistic hypotheses, and potential use in generating natural language tailored to specific user traits.

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.

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.