Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Foundations and Trends in Databases
Language-model-based pro/con classification of political text
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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The wikileaks documents or the economic crises in Ireland and Portugal are some of the controversial topics being played on the news everyday. Each of these topics has many different aspects, and there is no absolute, simple truth in answering questions such as: should the EU guarantee the financial stability of each member country, or should the countries themselves be solely responsible? To understand the landscape of opinions, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on these aspects of controversial topics. In this paper, we describe our system, named OpinioNetIt (pronounced similar to "opinionated"), which aims to automatically derive a map of the opinions-people network from news and other Web documents. We build this network as follows. First, we make use of a small number of generic seeds to identify controversial phrases from text. These phrases are then clustered and organized into a hierarchy of topics. Second, opinion holders are identified for each topic and their opinions (either supporting or opposing the topic) are extracted. Third, the known topics and people are used to construct a lexicon phrases indicating support or opposition. Finally, the lexicon is uses to identify more opinion holders, opinions and topics. Our system currently consists of approximately 30000 person-opinion-topic triples. Our evaluation shows that OpinioNetIt has high accuracy.