12,085 research outputs found
Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates
This study is a first, exploratory attempt to use quantitative semantics
techniques and topological analysis to analyze systemic patterns arising in a
complex political system. In particular, we use a rich data set covering all
speeches and debates in the UK House of Commons between 1975 and 2014. By the
use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show
that both members and parties feature specific roles within the system,
consistent over time, and extract global patterns indicating levels of
political cohesion. Our results provide a wide array of novel hypotheses about
the complex dynamics of political systems, with valuable policy applications
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM),
2017 IEEE/ACM International Conferenc
Rhetorical relations for information retrieval
Typically, every part in most coherent text has some plausible reason for its
presence, some function that it performs to the overall semantics of the text.
Rhetorical relations, e.g. contrast, cause, explanation, describe how the parts
of a text are linked to each other. Knowledge about this socalled discourse
structure has been applied successfully to several natural language processing
tasks. This work studies the use of rhetorical relations for Information
Retrieval (IR): Is there a correlation between certain rhetorical relations and
retrieval performance? Can knowledge about a document's rhetorical relations be
useful to IR? We present a language model modification that considers
rhetorical relations when estimating the relevance of a document to a query.
Empirical evaluation of different versions of our model on TREC settings shows
that certain rhetorical relations can benefit retrieval effectiveness notably
(> 10% in mean average precision over a state-of-the-art baseline)
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