Question classification using support vector machines
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Educational Question Answering based on Social Media Content
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Multimedia answering: enriching text QA with media information
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A comparative assessment of answer quality on four question answering sites
Journal of Information Science
Mining slang and urban opinion words and phrases from cQA services: an optimization approach
Proceedings of the fifth ACM international conference on Web search and data mining
Proceedings of the 21st international conference companion on World Wide Web
Understanding user intent in community question answering
Proceedings of the 21st international conference companion on World Wide Web
How many answers are enough? optimal number of answers for Q&A sites
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Predicting subjectivity orientation of online forum threads
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Evolutionary optimization for ranking how-to questions based on user-generated contents
Expert Systems with Applications: An International Journal
Joint question clustering and relevance prediction for open domain non-factoid question answering
Proceedings of the 23rd international conference on World wide web
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In this paper we begin to investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.