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, Man Lan


Anthology ID:
S16-1040
Volume:
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
Month:
June
Year:
2016
Address:
San Diego, California
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
256–261
Language:
URL:
https://aclanthology.org/S16-1040
DOI:
10.18653/v1/S16-1040
Bibkey:
Cite (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.
Cite (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 (Zhou et al., SemEval 2016)
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PDF:
https://aclanthology.org/S16-1040.pdf