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 fließenden Werten abgebildet. Diese feingranulare Darstellung ist für die dort gegebenen Fragestellungen erforderlich. Das Feld der “quantitativen Analyse” - der automatisierten Analyse von Texten - die der traditionellen qualitativen Analyse gegenüber steht, hat erst kürzlich 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 fließende Skalen. Darüber 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 einfließen 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 über 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

Multi-dimensional Analysis of Political Documents

Abstract. Automatic content analysis is more and more becoming an accepted research method in social science. In political science researchers are using party manifestos and transcripts of political speeches to analyze the positions of different actors. Existing approaches are limited to a single dimension, in particular, they cannot distinguish between the positions with respect to a specific topic. In this paper, we propose a method for analyzing and comparing documents according to a set of predefined topics that is based on an extension of Latent Dirichlet Allocation for inducing knowledge about relevant topics. We validate the method by showing that it can reliably guess which member of a coalition was assigned a certain ministry based on a comparison of the parties’ election manifestos with the coalition contract