23 research outputs found
Fine-grained position analysis for political texts
Meinungsanalyse auf politischen Textdaten hat im Bereich der Computerlinguistik in den letzten
Jahren stets an Bedeutung gewonnen. Dabei werden politische Texte zumeist in voneinander
diskrete Klassen unterteilt, wie zum Beispiel pro vs. contra oder links vs. rechts. In den
Politikwissenschaften dagegen werden bei der Analyse von politischen Texten Positionen auf
Skalen mit flieĂźenden Werten abgebildet. Diese feingranulare Darstellung ist fĂĽr die dort
gegebenen Fragestellungen erforderlich. Das Feld der “quantitativen Analyse” - der automatisierten
Analyse von Texten - die der traditionellen qualitativen Analyse gegenĂĽber steht, hat
erst kĂĽrzlich mehr Beachtung gefunden. Bisher werden Texte dabei zumeist lediglich durch
Worthäufigkeiten dargestellt und ohne jegliche Struktur modelliert.
Wir entwickeln in dieser Dissertation Ansätze basierend auf Methoden der Computerlinguistik
und der Informatik, die gegeignet sind, politikwissenschaftliche Forschungsfragen zu untersuchen.
Im Gegensatz zu bisherigen Arbeiten in der Computerlinguistik klassifizieren
wir nicht diskrete Klassen von Meinungen, sondern projizieren feingranulare Positionen auf
flieĂźende Skalen. DarĂĽber hinaus schreiben wir nicht Dokumenten ganzheitlich eine Position
zu, sondern bestimmen die Meinungen zu den jeweiligen Themen, die in den Texten enthalten
sind. Diese mehrdimensionale Meinungsanalyse ist nach unserem Kenntnisstand neu im
Bereich der quantitativen Analyse.
Was unsere Ansätze von anderen Methoden unterscheidet, sind insbesondere folgende zwei
Eigenschaften: Zum Einen nutzen wir Wissen aus externen Quellen, das wir in die Verfahren
einflieĂźen lassen - beispielsweise integrieren wir die Beschreibungen von Ministerien des Bundestags
als Definition von politischen Themenbereichen, mit welchen wir automatisch Themen
in Parteiprogrammen erkennen. Zum Anderen reichern wir unsere Verfahren mit linguistischem
Wissen ĂĽber Textkomposition und Dialogstruktur an. Somit gelingt uns eine tiefere
Modellierung der Textstruktur.
Anhand der folgenden drei Fragestellungen aus dem Bereich der Politikwissenschaften untersuchen
wir die Umsetzung der oben beschriebenen Methoden:
1. Multi-Dimensionale Positionsanalyse von Parteiprogrammen
2. Analyse von Themen und Positionen in der US-Präsidentschaftswahl
3. Bestimmen von Dove-Hawk-Positionen in Diskussionen der amerikanischen Zentralbank
Wir zeigen, dass die vorgestellten Lösungen erfolreich feingranulare Positionen in den jeweiligen
Daten erkennen und analysieren Möglichkeiten sowie Grenzen dieser zukunftsweisenden
Verfahren
Exploring youporn categories, tags, and nicknames for pleasant recommendations
YouPorn is one of the largest providers of adult content on the
web. Being free of charge, the video portal allows users - besides
watching - to upload, categorize, and comment on pornographic
videos. With this position paper, we point out the challenges of
analyzing the textual data offered with the videos. We report on
first experiments and problems with our
YouPorn dataset
, which we
extracted from the non-graphical content of the YP website. To gain
some insights, we performed association rule mining on the video
categories and tags, and investigated preferences of users based on
their nickname. Hoping that future research will be able to build
upon our initial experiences, we make the ready-to-use
YP dataset
publicly available
Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement
ABSTRACT People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products. In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features
TopFish: topic-based analysis of political position in US electoral campaigns
In this paper we present TopFish, a multilevel
computational method that integrates
topic detection and political scaling and
shows its applicability for a temporal aspect
analysis of political campaigns (preprimary
elections, primary elections, and
general elections). It enables researchers
to perform a range of multidimensional
empirical analyses, ultimately allowing
them to better understand how candidates
position themselves during elections, with
respect to a specific topic. The approach
has been employed and tested on speeches
from the 2008, 2012, and the (ongoing)
2016 US presidential campaigns
Classifying topics and detecting topic shifts in political manifestos
General political topics, like social security and foreign affairs, recur in electoral
manifestos across countries. The Comparative Manifesto Project collects and manually codes manifestos of political parties
from all around the world, detecting political topics at sentence level. Since manual coding is time-consuming and allows
for annotation inconsistencies, in this work
we present an automated approach to topical coding of political manifestos. We
first train three independent sentence-level
classifiers – one for detecting the topic
and two for detecting topic shifts – and
then globally optimize their predictions using a Markov Logic network. Experimental results show that the proposed global
model achieves high classification performance and significantly outperforms the
local sentence-level topic classifier
Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement
People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products.
In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task.
While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features