Methods for Constructing an Opinion Network for Politically Controversial Topics

The US presidential race, the re-election of President Hugo Chavez, and the economic crisis in Greece and other European countries are some of the controversial topics being played on the news everyday. To understand the landscape of opinions on political controversies, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on specific aspects of these topics. The work described in this thesis aims to automatically derive a map of the opinions-people network from news and other Web documents. The focus is on acquiring opinions held by various stakeholders on politically controversial topics. This opinions-people network serves as a knowledge-base of opinions in the form of hopinion holderi hopinioni htopici triples. Our system to build this knowledge-base makes use of online news sources in order to extract opinions from text snippets. These sources come with a set of unique challenges. For example, processing text snippets involves not just identifying the topic and the opinion, but also attributing that opinion to a specific opinion holder. This requires making use of deep parsing and analyzing the parse tree. Moreover, in order to ensure uniformity, both the topic as well the opinion holder should be mapped to canonical strings, and the topics should also be organized into a hierarchy. Our system relies on two main components: i) acquiring opinions which uses a combination of techniques to extract opinions from online news sources, and ii) organizing topics which crawls and extracts debates from online sources, and organizes these debates in a hierarchy of political controversial topics. We present systematic evaluations of the different components of our system, and show their high accuracies. We also present some of the different kinds of applications that require political analysis. We present some application requires political analysis such as identifying flip-floppers, political bias, and dissenters. Such applications can make use of the knowledge-base of opinions