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This paper introduces a novel deep learning framework including alexicon-based approach for sentence-level prediction of sentiment labeldistribution. We propose to first apply semantic rules and then use a DeepConvolutional Neural Network (DeepCNN) for character-level embeddings in orderto increase information for word-level embedding. After that, a BidirectionalLong Short-Term Memory Network (Bi-LSTM) produces a sentence-wide featurerepresentation from the word-level embedding. We evaluate our approach on threeTwitter sentiment classification datasets. Experimental results show that ourmodel can improve the classification accuracy of sentence-level sentimentanalysis in Twitter social networking.

A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking