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This paper describes our participation in the TREC 2008 Blog Track. For the opinion task, we made an opinion retrieval model that consists of preprocessing, topic retrieval, opinion finding, and sentiment classification parts. For topic retrieval, our system is based on the passage-based retrieval model and feedback. For the opinion analysis, we created a(More)
The analysis of query logs from blog search engines show that news-related queries occupy a significant portion of the logs. This raises a interesting research question on whether the blogosphere can be used to identify important news stories. In this paper, we present novel approaches to identify important news story headlines from the blogosphere for a(More)
We describe an opinion analysis system developed for Multilingual Opinion Analysis Task at NTCIR7. Given a topic and relevant newspaper articles, our system determines whether a sentence in the articles carries an opinion, if so, then extract the polarity and holder of the opinion. Our system uses subjectivity lexicons to score the sentiment weight of a(More)
This paper describes our participation in the TREC 2009 Blog Track. Our system consists of the query likelihood component and the news headline prior component, based on the language model framework. For the query likelihood , we propose several approaches to estimate the query language model and the news headline language model. We also suggest two(More)
This paper describes our participation in the TREC 2010 Blog Track. For the Top Stories Identification Task, we explore the relationship among news events, news stories and blog posts. We first extract important news events from the TRC2 corpus using a probabilistic mixture model. Then, we propose a prob-abilistic approach to identify top news stories.(More)
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