Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Classifying latent user attributes in twitter
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Identifying sarcasm in Twitter: a closer look
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Probably the major approach to making predictions or recommendations about user behavior is by pairing unambiguous indicators of preference with attributes of the users who have given those indications. However, since the necessary preference data is often difficult to obtain, it is considered very valuable and often held closely by vendors and advertisers. On the other hand, while Twitter and other social media platforms provide a wealth of data about users by way of what they say or tweet, who or what they like or follow, etc., little work has been done to combine these data with indicators of preference for purposes of prediction or recommendation. In this paper we present a novel approach to mining preference data from natural language expressions in social media, which are then extrapolated to other individuals whose preferences are not known through predictive modeling. As an application for this approach, we describe the implementation of Tweetcast Your Vote, a publicly accessible system that predicted the voting decisions of Twitter users in the 2012 U.S. presidential election.