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%0 Conference Proceedings
%T Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training
%A Xiang, Bing
%A Zhou, Liang
%Y Toutanova, Kristina
%Y Wu, Hua
%S Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2014
%8 June
%I Association for Computational Linguistics
%C Baltimore, Maryland
%F xiang-zhou-2014-improving
%R 10.3115/v1/P14-2071
%U https://aclanthology.org/P14-2071
%U https://doi.org/10.3115/v1/P14-2071
%P 434-439

Markdown (Informal)

[Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training](https://aclanthology.org/P14-2071) (Xiang & Zhou, ACL 2014)

• Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training (Xiang & Zhou, ACL 2014)

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.