Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training

Bing Xiang, Liang Zhou


Anthology ID:
P14-2071
Volume:
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
June
Year:
2014
Address:
Baltimore, Maryland
Editors:
Kristina Toutanova, Hua Wu
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
434–439
Language:
URL:
https://aclanthology.org/P14-2071
DOI:
10.3115/v1/P14-2071
Bibkey:
Cite (ACL):
Bing Xiang and Liang Zhou. 2014. Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 434–439, Baltimore, Maryland. Association for Computational Linguistics.
Cite (Informal):
Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training (Xiang & Zhou, ACL 2014)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/P14-2071.pdf