Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Building simulated queries for known-item topics: an analysis using six european languages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
On ranking controversies in wikipedia: models and evaluation
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Domain-specific sentiment analysis using contextual feature generation
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
A generate-and-test method of detecting negative-sentiment sentences
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Leveraging editor collaboration patterns in wikipedia
Proceedings of the 23rd ACM conference on Hypertext and social media
Identifying controversial articles in Wikipedia: a comparative study
Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration
Detecting controversy on the web
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We tackle the problem of automatically detecting controversial issues and their subtopics from news articles. We define a controversial issue as a concept that invokes conflicting sentiments or views and a subtopic as a reason or factor that gives a particular sentiment or view to the issue. Conforming to the definitions, we propose a controversial issue detection method that considers the magnitude of sentiment information and the difference between the amounts of two different polarities. For subtopic identification, candidate phrases are generated and checked for containing five different features, some of which attempts to capture the relationship between the identified issue phrase and the candidate subtopic phrase. Through an experiment and analysis using the MPQA corpus consisting of news articles, we found that the proposed method is promising for both of the tasks although many additional research issues remain to be tapped in the future.