%0 Generic %1 nguyen2017neural %A Nguyen, Huy %A Nguyen, Minh-Le %D 2017 %K character cnn deep_learning rnn sentiment twitter %T A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking %U http://arxiv.org/abs/1706.08032 %X This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.

@misc{nguyen2017neural, abstract = {This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.}, added-at = {2018-02-28T20:42:14.000+0100}, author = {Nguyen, Huy and Nguyen, Minh-Le}, biburl = {https://www.bibsonomy.org/bibtex/23ee3b1ceeff2c95a0d73a97f0e706af5/dallmann}, description = {1706.08032.pdf}, interhash = {3dc6979487ea0e2972fa1ceaabd9c212}, intrahash = {3ee3b1ceeff2c95a0d73a97f0e706af5}, keywords = {character cnn deep_learning rnn sentiment twitter}, note = {cite arxiv:1706.08032Comment: PACLING Conference 2017, 6 pages}, timestamp = {2018-02-28T20:42:14.000+0100}, title = {A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking}, url = {http://arxiv.org/abs/1706.08032}, year = 2017 }