Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
An unobtrusive behavioral model of "gross national happiness"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Journal of the American Society for Information Science and Technology
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Liars and saviors in a sentiment annotated corpus of comments to political debates
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape
Social Science Computer Review
Predicting political preference of Twitter users
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Microblogging websites such as Twitter offer a wealth of insight into a population's current mood. Automated approaches to identify general sentiment toward a particular topic often perform two steps: Topic Identification and Sentiment Analysis. Topic Identification first identifies tweets that are relevant to a desired topic (e.g., a politician or event), and Sentiment Analysis extracts each tweet's attitude toward the topic. Many techniques for Topic Identification simply involve selecting tweets using a keyword search. Here, we present an approach that instead uses distant supervision to train a classifier on the tweets returned by the search. We show that distant supervision leads to improved performance in the Topic Identification task as well in the downstream Sentiment Analysis stage. We then use a system that incorporates distant supervision into both stages to analyze the sentiment toward President Obama expressed in a dataset of tweets. Our results better correlate with Gallup's Presidential Job Approval polls than previous work. Finally, we discover a surprising baseline that outperforms previous work without a Topic Identification stage.