Language-model-based pro/con classification of political text

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http://hdl.handle.net/20.500.12358/25198

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Title	Language-model-based pro/con classification of political text
Abstract	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.
Authors	Awadallah, Rawia Ramanath, Maya Weikum, Gerhard
Type	Journal Article
Date	2010
Subjects	experimentation language models text classification h31 [information storage and retrieval]: content analysis and indexing-linguistic processing general terms algorithms sentiment analysis
Published in	Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Publisher	ACM
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Collections	Staff Publications- Faculty of Information Technology [181]

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