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ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features
Zhihua Zhang
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Guoshun Wu
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Man Lan
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Paper Details:
Month: June
Year: 2015
Location: Denver, Colorado
Venue:
*SEMEVAL |
Citations
URL
Ultradense Word Embeddings by Orthogonal Transformation
Sascha Rothe
|
Sebastian Ebert
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Hinrich Schütze
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I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter
Zhengchen Zhang
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Chen Zhang
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Fuxiang Wu
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Dong-Yan Huang
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Weisi Lin
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Minghui Dong
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UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination
Ladislav Lenc
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Pavel Král
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Václav Rajtmajer
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CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification
Raj Kumar Gupta
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Yinping Yang
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http://www.cs.uic.edu/liub/FBS/sentiment-
http://www.wjh.harvard.edu/inquirer/homecat.htm
http://anthology.aclweb.org//S/S13/S13-2.pdf#page=444
http://mpqa.cs.pitt.edu/
http://sentiwordnet.isti.cnr.it/
http://www.umiacs.umd.edu/saif/WebDocs/NRC-Hashtag-
http://help.sentiment140.com/for-students/
Field Of Study
Linguistic Trends
Embeddings
Task
Tagging
Sentiment Analysis
Language
English
Dataset
News
Twitter
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