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%0 Conference Proceedings
%T 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
%A Zhou, Yunxiao
%A Zhang, Zhihua
%A Lan, Man
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Cer, Daniel
%Y Jurgens, David
%Y Nakov, Preslav
%Y Zesch, Torsten
%S Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
%D 2016
%8 June
%I Association for Computational Linguistics
%C San Diego, California
%F zhou-etal-2016-ecnu
%R 10.18653/v1/S16-1040
%U https://aclanthology.org/S16-1040
%U https://doi.org/10.18653/v1/S16-1040
%P 256-261

Markdown (Informal)

[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](https://aclanthology.org/S16-1040) (Zhou et al., SemEval 2016)

• 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 (Zhou et al., SemEval 2016)

ACL

• Yunxiao Zhou, Zhihua Zhang, and Man Lan. 2016. 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. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 256–261, San Diego, California. Association for Computational Linguistics.