Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Republic.com
NewsInEssence: summarizing online news topics
Communications of the ACM - The digital society
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
CollabSum: exploiting multiple document clustering for collaborative single document summarizations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Mitigating media bias: a computational approach
Proceedings of the hypertext 2008 workshop on Collaboration and collective intelligence
Rich interfaces for reading news on the web
Proceedings of the 14th international conference on Intelligent user interfaces
NewsCube: delivering multiple aspects of news to mitigate media bias
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the ACM 2011 conference on Computer supported cooperative work
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Aspect-level news browsing provides readers with a classified view of news articles with different viewpoints. It facilitates active interactions with which readers easily discover and compare diverse existing biased views over a news event. As such, it effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of articles. We first demonstrate the feasibility of aspect-level news browsing through user studies. We then deeply look into the news article production process and develop framing cycle-aware clustering. The evaluation results show that the developed method performs classification more accurately than other methods.