Twitter Sentiment Analysis with deep convolutional neural networks

SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2015, Pages 959-962

  Severyn, Aliaksei ^a   Moschitti, Alessandro ^b,c  

^a GOOGLE   (United Kingdom)

^b QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

^c UNIVERSITY OF TRENTO   (Italy)

Author keywords

Convolutional neural networks; Twitter Sentiment Analysis

Indexed keywords

CONVOLUTION; INFORMATION RETRIEVAL; NEURAL NETWORKS; SOCIAL NETWORKING (ONLINE);

ACCURATE MODELING; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; EMBEDDINGS; LANGUAGE MODEL; SENTIMENT ANALYSIS; SUPERVISED TRAININGS; SYSTEM EVALUATION;

DATA MINING;

EID: 84953807567     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2766462.2767830     Document Type: Conference Paper

References (12)

1. A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. In CS224N Project Report, Stanford, 2009.
2. N. Kalchbrenner, E. Grefenstette, and P. Blunsom. A convolutional neural network for modelling sentences. In ACL, 2014.
3. Y. Kim. Convolutional neural networks for sentence classification. In EMNLP, 2014.
4. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
5. S. M. Mohammad, S. Kiritchenko, and X. Zhu. Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. In Semeval, 2013.
6. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010.
7. J. W. Ronan Collobert. A unified architecture for natural language processing: deep neural networks with multitask learning. In ICML, 2008.
8. Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. A latent semantic model with convolutional-pooling structure for information retrieval. CIKM, 2014.
9. Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. Learning semantic representations using convolutional neural networks for web search. In WWW, 2014.
10. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929-1958, 2014.
11. S. M. M. Xiaodan Zhu, Svetlana Kiritchenko. Nrc-canada-2014: Recent improvements in sentiment analysis of tweets, and the Voted Perceptron. In SemEval, 2014.
12. M. D. Zeiler. Adadelta: An adaptive learning rate method. CoRR, 2012.