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TweeNLP
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Political Tendency Identification in Twitter using Sentiment Analysis Techniques
Ferran Pla
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Lluís-F. Hurtado
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Paper Details:
Month: August
Year: 2014
Location: Dublin, Ireland
Venue:
COLING |
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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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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Classification of Moral Foundations in Microblog Political Discourse
Kristen Johnson
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Dan Goldwasser
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NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Christos Baziotis
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Athanasiou Nikolaos
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Alexandra Chronopoulou
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Athanasia Kolovou
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Georgios Paraskevopoulos
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Nikolaos Ellinas
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Shrikanth Narayanan
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Alexandros Potamianos
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Identifying Stance by Analyzing Political Discourse on Twitter
Kristen Johnson
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Dan Goldwasser
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Aligning Entity Names with Online Aliases on Twitter
Kevin McKelvey
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Peter Goutzounis
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Stephen da Cruz
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Nathanael Chambers
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NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
Alexandra Chronopoulou
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Aikaterini Margatina
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Christos Baziotis
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Alexandros Potamianos
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http://creativecommons.org/licenses/by/4.0/
http://www.daedalus.es/TASS2013
https://github.com/brendano/tweetmotif
http://nlp.lsi.upc.edu/freeling/
http://www.cs.waikato.ac.nz/ml/weka/
http://www.cs.iastate.edu/
Field Of Study
Task
Tagging
Word Sense Disambiguation
Named Entity Recognition
Sentiment Analysis
Text Categorization
Language
English
Spanish
Dataset
Social Media
Twitter
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