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ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter
Yunxiao Zhou
|
Zhihua Zhang
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Man Lan
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Paper Details:
Month: June
Year: 2016
Location: San Diego, California
Venue:
*SEMEVAL |
Citations
URL
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
Ji Ho Park
|
Peng Xu
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Pascale Fung
|
http://www.cs.uic.edu/liub/FBS/sentiment-
http://www.wjh.harvard.edu/inquirer/homecat.htm
http://www2.imm.dtu.dk/pubdb/views/publication
http://anthology.aclweb.org//S/S13/S13-2.pdf#page=444
http://mpqa.cs.pitt.edu/
http://www.saifmohammad.com/WebPages/lexicons.html
http://www.umiacs.umd.edu/saif/WebDocs/NRC-Hashtag-
http://help.sentiment140.com/for-students/
https://code.google.com/archive/p/word2vec
http://alt.qcri.org/semeval2016/task4/data/uploads/eval.pdf
https://www.csie.ntu.edu.tw/
Field Of Study
Linguistic Trends
Embeddings
Task
Tagging
Sentiment Analysis
Approach
Deep Learning
Language
English
Dataset
Twitter
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