Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Computing political preference among twitter followers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Leveraging editor collaboration patterns in wikipedia
Proceedings of the 23rd ACM conference on Hypertext and social media
Learning for microblogs with distant supervision: political forecasting with Twitter
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle
ACL '12 Proceedings of the ACL 2012 System Demonstrations
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We study the problem of predicting the political preference of users on the Twitter network, showing that the political preference of users can be predicted from their interaction with political parties. We show this by building prediction models based on a variety of contextual and behavioural features, training the models by resorting to a distant supervision approach and considering party candidates to have a predefined preference towards their parties. A language model for each party is learned from the content of the tweets by the party candidates, and the preference of a user is assessed based on the alignment of user tweets with the language models of the parties. We evaluate our work in the context of Alberta 2012 general election, and show that our model outperforms, in terms of the F-measure, sentiment and text classification approaches and is in par with the human annotators. We further use our model to analyze the preference changes over the course of the election campaign and report results that would be difficult to attain by human annotators.