The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Who said what to whom?: capturing the structure of debates
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Knowledge-rich Word Sense Disambiguation rivaling supervised systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Exploiting structured ontology to organize scattered online opinions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
An analysis of perspectives in interactive settings
Proceedings of the First Workshop on Social Media Analytics
Collective classification of congressional floor-debate transcripts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Mining contrastive opinions on political texts using cross-perspective topic model
Proceedings of the fifth ACM international conference on Web search and data mining
Harmony and dissonance: organizing the people's voices on political controversies
Proceedings of the fifth ACM international conference on Web search and data mining
Opinions network for politically controversial topics
Proceedings of the first edition workshop on Politics, elections and data
Modeling interaction features for debate side clustering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
We consider the problem of automatically classifying quotations about political debates into both topic and polarity. These quotations typically appear in news media and online forums. Our approach maps quotations onto one or more topics in a category system of political debates, containing more than a thousand fine-grained topics. To overcome the difficulty that pro/con classification faces due to the brevity of quotations and sparseness of features, we have devised a model of quotation expansion that harnesses antonyms from thesauri like WordNet. We developed a suite of statistical language models, judiciously customized to our settings, and use these to define similarity measures for unsupervised or supervised classifications. Experiments show the effectiveness of our method.