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ORCIDTSA@CIKM 2009: Hong Kong, SAR, China
- Maojin Jiang, Bei Yu:
Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, TSA '09, Hong Kong, SAR, China, November 6, 2009. ACM 2009, ISBN 978-1-60558-805-6
Topic and sentiment
- Julian Brooke, Matthew Hurst:
Patterns in the stream: exploring the interaction of polarity, topic, and discourse in a large opinion corpus. 1-8 - Neil O'Hare, Michael Davy, Adam Bermingham, Paul Ferguson, Páraic Sheridan, Cathal Gurrin
, Alan F. Smeaton:
Topic-dependent sentiment analysis of financial blogs. 9-16 - Scott Nowson:
Scary films good, scary flights bad: topic driven feature selection for classification of sentiment. 17-24 - Alexandra Balahur, Ester Boldrini, Andrés Montoyo, Patricio Martínez-Barco:
Towards the definition of requirements for mixed fact and opinion question answering systems. 25-28
Challenge: corpus, domain, irony
- Luís Sarmento, Paula Carvalho, Mário J. Silva, Eugénio Oliveira:
Automatic creation of a reference corpus for political opinion mining in user-generated content. 29-36 - Yoonjung Choi, Youngho Kim, Sung-Hyon Myaeng:
Domain-specific sentiment analysis using contextual feature generation. 37-44 - Jonathon Read, John Carroll:
Weakly supervised techniques for domain-independent sentiment classification. 45-52 - Paula Carvalho, Luís Sarmento, Mário J. Silva, Eugénio Oliveira:
Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-). 53-56
Application
- Niklas Jakob, Stefan Hagen Weber, Mark-Christoph Müller, Iryna Gurevych:
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. 57-64 - Jingbo Zhu, Muhua Zhu, Huizhen Wang, Benjamin K. Tsou:
Aspect-based sentence segmentation for sentiment summarization. 65-72 - Takayoshi Okamoto, Tetsuya Honda, Koji Eguchi:
Locally contextualized smoothing of language models for sentiment sentence retrieval. 73-80 - Tun Thura Thet, Jin-Cheon Na, Christopher S. G. Khoo, Subbaraj Shakthikumar:
Sentiment analysis of movie reviews on discussion boards using a linguistic approach. 81-84
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