Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
How can you say such things?!?: recognizing disagreement in informal political argument
LSM '11 Proceedings of the Workshop on Languages in Social Media
Diversifying search results of controversial queries
Proceedings of the 20th ACM international conference on Information and knowledge management
OpinioNetIt: understanding the opinions-people network for politically controversial topics
Proceedings of the 20th ACM international conference on Information and knowledge management
Harmony and dissonance: organizing the people's voices on political controversies
Proceedings of the fifth ACM international conference on Web search and data mining
Mining contentions from discussions and debates
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Opinions network for politically controversial topics
Proceedings of the first edition workshop on Politics, elections and data
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Given a controversial political topic, our aim is to classify documents debating the topic into pro or con. Our approach extracts topic related terms, pro/con related terms, and pairs of topic related and pro/con related terms and uses them as the basis for constructing a pro query and a con query. Following standard LM techniques, a document is classified as pro or con depending on which of the query likelihoods is higher for the document. Our experiments show that our approach is promising.