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Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter
Micol Marchetti-Bowick
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Nathanael Chambers
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Paper Details:
Month: April
Year: 2012
Location: Avignon, France
Venue:
EACL |
Citations
URL
“All I know about politics is what I read in Twitter”: Weakly Supervised Models for Extracting Politicians’ Stances From Twitter
Kristen Johnson
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Dan Goldwasser
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TweeTime : A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter
Jeniya Tabassum
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Alan Ritter
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Wei Xu
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Improving the Inference of Implicit Discourse Relations via Classifying Explicit Discourse Connectives
Attapol Rutherford
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Nianwen Xue
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On the Automatic Learning of Sentiment Lexicons
Aliaksei Severyn
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Alessandro Moschitti
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Cross-Domain Mining of Argumentative Text through Distant Supervision
Khalid Al-Khatib
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Henning Wachsmuth
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Matthias Hagen
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Jonas Köhler
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Benno Stein
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Who caught a cold ? - Identifying the subject of a symptom
Shin Kanouchi
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Mamoru Komachi
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Naoaki Okazaki
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Eiji Aramaki
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Hiroshi Ishikawa
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Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter
Kristen Johnson
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Di Jin
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Dan Goldwasser
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USNA: A Dual-Classifier Approach to Contextual Sentiment Analysis
Ganesh Harihara
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Eugene Yang
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Nathanael Chambers
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Language Technology for Agile Social Media Science
Simon Wibberley
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David Weir
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Jeremy Reffin
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Optimising Agile Social Media Analysis
Thomas Kober
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David Weir
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Identifying Stance by Analyzing Political Discourse on Twitter
Kristen Johnson
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Dan Goldwasser
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http://gallup.com/poll/113980/gallup-daily-obama-job-
http://www.usna.edu/cs/nchamber/data/twitter
Field Of Study
Task
Sentiment Analysis
Social Science
Language
English
Dataset
Social Media
Twitter
Blogs
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